Speaker 2: (00:01)
It's not about knowing five or 10 different programming languages. It's not about knowing like five or 10 different algorithms or anything like that, but it's about bringing your unique perspective, your unique story, telling your unique experience to the table, to the analysis, to how you think about these problems. I think when when women, when we started censor ourselves or not really giving the best we can to our work, into the people around us. 

Speaker 3: (00:25)
[inaudible] 

Speaker 3: (00:32)
[inaudible] 

Speaker 1: (00:48)
what's up everyone? Thank you so much for tuning into the artists of data science podcast. My goal with this podcast is to share the stories and journeys of the thought leaders in data science, the artists who are creating value for our field through the content they're creating, the work they're doing and the positive impact they're having within their organizations, industries, society in the art. Update a science as a whole. I can't even begin to express how excited I am that you're joining me today. My name is Harpreet Sahota and I'll be your host as we talk to some of the most amazing people in data science. Today's episode is brought to you by data science screen. Job. If you're wondering what it takes to break into the field of data science, checkout DSDJ. Co/artists with  an S or an invitation to a free webinar where we'll give you tips on how to land your first job in data science. 

Speaker 1: (01:40)
I've also got a free open mastermind Slack community called the artists of data science loft that I encourage everyone listening to join. I'll make myself available to you for questions on all things data science and keep me posted on the biweekly open office hours that I'll be hosting our community. Check that out@artofdatascienceloft.slack.com community is super important and I'm hoping you guys will join the community. We'll keep each other motivated, keep each other in the loop on what's going on with our own journeys so that we can learn, grow, and get better together. Let's ride this beat out into another awesome episode and don't forget to subscribe, follow like love, great interview with the show. 

Speaker 3: (02:25)
[inaudible]. 

Speaker 1: (02:41)
Our guest today is someone I truly respect and admire and have the honor of calling a colleague, one of the hardest working, motivated and genuinely good hearted people I know she's a decision scientist, passionate about crafting bespoke data science products for high growth startups and tech companies. She's got over five years of experience leveraging data science, machine learning, and advanced analytics to drive operations and strategies. She's worked as some of the most innovative startups in Silicon Valley, including walk me and Autodesk. She's currently a data scientist at Lovango where she has an opportunity to collaborate closely with the leaders there to deliver a world-class decision support from growth marketing. She's helped increase *** reach and impact for people living with chronic and behavioral conditions when she's not crushing it as a data scientist focused on growth on all levels, whether it's personal, physical, or professional. She's also a mentor for various data science programs, including the one I'm a part of, data science dream job and provides consulting services for startups, building ML and AI products in all industries from real estate to fitness. She's got a true passion for life in a raging fire in her belly. And now he's being able to spend time and share the joys of life with the people, places and activities she cares most about. So please help me in welcoming our guest today, a mentor, instructor, writer, consultant, friend and colleague. Makiko Bazeli Makiko thank you so much for taking time out of your schedule to be here on the podcast. I really appreciate you being here. 

Speaker 2: (04:05)
Oh, thank you so much. Harpreet really, I mean, I appreciate the opportunity to, um, you know, talk a little bit more about my story, um, and hopefully inspire people who, you know, thinks to themselves, you know, day in, day out, man. Like I, I really wish I could do this work. I just, I don't know how to get into the field. Um, you know, I wish there were people like me. You know, what, what, what do I do? 

Speaker 1: (04:26)
Yeah. And your journey into data science, I think is one of the most awesome stories of just straight, raw hustle, motivation and desire to make an impact that I've heard in a long time. And I think a lot of people can learn from your story. So I was wondering if you could walk me through the career path that's brought you to where you are and how 

Speaker 2: (04:45)
when I, when I talk about my story, um, you know, the three sort of themes I like to emphasize for people are, um, you know, first off that, you know, there's opportunity all around you if you just open your eyes. Um, the second thing I really like to sort of emphasize is the sort of the MacGyver mindset. Yeah. Um, so for people who don't know a MacGyver, he was this TV character who was known for coming up with really crazy, insane, innovative ways to, um, you know, solve problems and, you know, get it, get himself like out of prison, using, um, like scissors, chewing gum and like a shoe string or you know, you know, fighting terrorists using, um, like a paper plane and you know, an umbrella toothpick or something like that. So the second thing is really the MacGyver principle, which is that, um, you know, sometimes you, you just have to make the best of the situation using like really different insane pieces. 

Speaker 2: (05:49)
You wouldn't think we'd come together, um, to just like keep moving forward. And then, you know, the third part or the third thing I like to always emphasize is the, the concept of, um, getting behind your fear. The psychological prison is actually the most effective prison. Viktor Frankl he wrote, he was this Holocaust survivor who was known for his book, I believe in search of meaning. And you know, he writes a lot about essentially the, the psychological, psychological toll on the mind that I'm being, um, you know, in a camp took, but also this idea that, um, the human emotion, the human mind, that that's really the last place of freedom of expression that individuals have. It's their most powerful tool of, of expression. Um, and one which will allow them to kind of, you escape the horror around them. So those are the three things. 

Speaker 2: (06:45)
I liked him size when talking about my story because that's really kind of how I've approached every opportunity. So, you know, when I was first growing up, um, you know, I, I grew up in a typical, I'd say, you know, middle-class like Asian tiger family situation where, you know, as I was going through high school, um, the, the kind of the goal for me was to, you know, pick one of the Holy Trinity, right? Which was doctor, lawyer, engineer. Um, that was kind of the expectation for me going to college, um, was that ideally I would go into the medical profession as a doctor. Um, and so consequently, you know, I took like every AP honors class in high school you could think of. I was the captain of the fencing team and the, the head of the paper and you know, won a bunch of scholarships and you know, all this stuff. 

Speaker 2: (07:37)
So, right. So I went to college, um, gone to UC for biomedical engineering. Um, you know, I, I thought I had this passion for becoming a doctor and sort of saving the world. And then, you know, I spent for five years, I spent five years, um, essentially bouncing around from major to major because at some point I kind of realized that, um, you know, first off, I had this like, I think idea of like scale, which is that the medical industry and field scales, right? And as, as we can see with the Kobe aid crisis, that's not quite true, right? Um, doctors cannot scale beyond the patients that they're seeing. Healthcare systems cannot scale beyond their capacity. So, you know, when I, when I went into the program and I was doing this aid work in other countries like Honduras, you know, I, I just kind of experience a lot of the syrup you're  that sometimes comes in the medical field and I was very disillusioned, you know, so I bounced around from major, major, I'm just trying to figure out what was my place. 

Speaker 2: (08:42)
Um, finally landed in like anthropology and economics. Um, because it just, it made sense nowadays. Uh, the kind of work that I do, we actually call it decision science. Um, but at the time, you know, in the anther econ program, I was studying game theory. I was studying, um, you know, behavioral ecology. I was studying the evolution of the human diet. Um, I was studying all these different topics, which, um, it didn't seem to make sense at the time in terms of a cohesive career, you know, so, you know, gone into college at 2008, uh, zoom forward to choose out 2013. Right. Graduate with, um, anthropology and economics and then, um, you know, graduated into the market with basically no marketable skills whatsoever. Um, you know, a lot of people telling me, like a lot of the students I work with sometimes they're like, well, you had a, you know, did you have like a quantitative background, you know, et cetera. 

Speaker 2: (09:43)
It's like, no, actually I didn't when I first entered the, the economy, you know, as a new grad, um, you know, like I couldn't code in Python or R, I could kind of do, I could do assignments in R, right? Like the kind where it's like, Oh, you're given a data set and um, you know, give us these descriptive stats. But the code was pretty bad cause I was just used to solving problems in academia. Um, definitely no SQL knowledge. Didn't know what machine learning was. Not at all, didn't, hadn't even heard of data science at that point in 2013. Yeah, I just had no skills, definitely no visit knowledge, you know, and so that was tough. It, you know, I struggled for months to try to find a job, um, you know, move back from San Diego and Senator scope to move back on my parents and then, you know, just tried getting kind of any job that I could, you know, the first, so the first job I got was working at a hair salon. 

Speaker 2: (10:37)
I went to the interview and the, the salon owner looked at me and she went, you know, with that hair coat, we, we really can't have you representing the salon and this mess of a bowl cut that I had done by myself. Yeah, I know. It's real. It's pretty bad. Um, it was pretty bad. And so, uh, you know, she said that and I was like, but I can do numbers. And she's like, Oh, you know, we actually need someone to keep the books for us. Um, you know, and, uh, are you willing to sweep a pair and you know, change the shampoo bottles and do front desks. So that was my job. Um, so should I give me for that? And I was static, you know, all my friends, they from college, they were already working for, you know, the big tech companies and you know, financial firms. 

Speaker 2: (11:22)
And uh, I was like  now I have a job and a paycheck. And that was a pretty, that was an interesting point in my life too, is it was an inflection point. Um, you know, cause I basically had gone through five years of college. A man should come out with a GPA that's so embarrassing that I don't actually have it publicly posted anywhere. Definitely below three, three. Oh, I can tell you, but it was an embarrassingly low GPA. So I really felt like a failure, you know, and there's a lot of things to write. Like I had a like $0 million in my bank account, didn't have a driver's license, was living with my parents at 23. All these sorts of things were going on. So, Oh, that first job, but like busted my bud doing that job and um, it was 20 hours a week, you know, as minimum. 

Speaker 2: (12:07)
Um, so you know, what I would do during that time is that since hair salons aren't open on, uh, they don't open Sundays and Mondays is, I would use that time to go take community college classes at local JC. You know, some of those classes were in like, uh, my MYXQL  or database design. Um, sometimes they are in GIS, sometimes they were in dance classes, but I figured since essentially my time off was one, everyone else didn't have their time off. I would go kind of learn and try to enrich myself. Um, senior around that point allow my customers, I'm at this hair salon. It was a pretty unique salon actually because a lot of our clients were actually like, uh, it kind of sr tech executives. And so I really got to understand and kind of experience what the tech industry was like. And at some point I said to myself, you know what? 

Speaker 2: (12:57)
I want to go try. I want to see what startups are all about. What are these startups? You know, I would interact with these clients and they would tell me about the work they were doing. At some point I kinda was asking myself, I'm like, well, why can't I be in your shoes? You know, like, what's, what's, what's the difference between you and me? Really? And it that point, you know, the difference was only time and experience. So I said, okay, like am I putting the time and I work on the experience and eventually I could kind of get there too. So got my first job like as a growth hacker, uh, for a early stage startup. Um, and even though that one kind of eventually pivoted after seven months, you know, it was like this really kind of, um, fast paced introduction to growth, growth hacking, um, startups to data. 

Speaker 2: (13:43)
And it was where I kind of got my, my first sort of first experience doing any kind of data analysis that ways I'd say in 2014, um, was when I was working at that startup. And then from there, you know, going back to those three themes, uh, first off, like the MacGyver principle, the kind of getting behind your fear. Essentially. After that I would kind of, you know, I'd work at, I worked, I'd work at that at a company, get a little bit more experience doing data analysis, and then I would see kind of this like other, this, this point on the horizon. You know, it's kind of like, if you're this like Explorer going through a jungle, right? You'll locate a vantage point. You'll say like, I want to hit that mountain, or I want to hit that peak. You'll kind of make your way through the jungle. 

Speaker 2: (14:29)
You'll just wait through all the, the, the bushes and the vines and the trees. You'll get there and then you're like, great, but now I want to get to that other mountain. And so that's essentially what my career looked like from 2014 to, you know, where I am now. Um, after that startup went to another company anti-piracy where as doing more data analysis work, I'm still in AR, I'm still mostly doing like ad hoc reports. Um, after that then I, you know, when to solar company where I was doing sales and financial analysis after that was then Autodesk where I got hired for more of a hybrid data analysts, data scientists role. I'm working on their construction product, uh, looking at proc adoption usage. Then after that was the walk me experience, whereas working as a senior data analyst for, um, sales, doing a lot of sales modeling, uh, forecasting. 

Speaker 2: (15:25)
Um, and then finally to my data scientist, uh, job at Lovango where I currently work on growth. Um, you know, which is a super interesting area and it's also, it comes full circle. You know, my first job in the server world was working on growth, didn't have a clue about it, you know, now, right. Right now at Lovango, my work is, you know, helping to empower our growth team to uh, you know, enroll members into our health programs. Um, whether it's diabetes, hypertension, uh, weight management, prediabetes, and then also to help ensure that those members are, are really making use, Oh, those platforms and programs even right now during, during COVID

Speaker 1: (16:06)
Wow. That's awesome. That's such a interesting path into data science and thank you for sharing all that being so vulnerable with the kind of the ups and downs on your, on your path here. And I think the real key to success is being able to draw on all these previous experience that are seeming seemingly unrelated to data science, but you kind of package it and put them together in a way that's gonna be able to draw on your previous experience using, you know, that kind of MacGyver principle you're talking about too. Be successful in any situation that's in front of you. One thing that's really important, I think to convey to any aspiring data scientist is that it's not just about math programming or technical skills, right? 
Speaker 1
WhatÕs up artists, check out our free open mastermind Slack channel, the artists of data science loft at art of data science, loft.slack.com I'll keep you posted on the biweekly open office hours that I'll be hosting. And it's a great environment and community for all of us to talk all things, data science. Look forward to seeing you there. 

What would you say is one thing that you picked up from, it'll work before data science that you feel is most definitely contributing to your current success? Uh, whether that's, you know, a soft skill or, or, or technical skill. 

Speaker 2: (17:31)
Yeah, absolutely. So I would say actually, um, and you're, they've done similar research in terms of uh, looking at engineers who eventually become, uh, leaders in their space or like a, you know, a chief technology officer or a direct, you know, a director of engineering. And so at some point your technical skills don't really matter as much as some of the softer skills. And you know, one skillset that to me is actually kind of my value proposition is my product and business knowledge. So or another way to say it is thinking like a business owner, thinking like a business or product owner. Um, so, so often, you know, even like right now, even when like for example, I'm helping interview data science candidates when we get their resumes right and when they, they all get through the tech screen, so they have some kind of, you know, minimum sort of level of MYXQL and Python coding. 

Speaker 2: (18:29)
But the canvas that really, really stick out are the ones who are immediately able to, uh, first off, you know, take a business problem that we present. They're able to frame it up in a way that it becomes either, you know, um, exploratory data analysis problem or, um, you know, a day science modeling problem. Um, and then they're able to connect the strategy and the outcome of that problem back into the business. And so I think a lot of data scientists, they, um, you know, there's a lot of hype around like the latest tool for example, like, you know, what kind of like GPS should be using, what kind of machine should we use? Should you be using, um, you know, should you, should you be using in like RNN versus CNN. Um, and the reality is that for Sokoloff companies are not actually that data savvy. 

Speaker 2: (19:20)
They're not quite at the point where they're very mature in terms of being able to leverage data science, machine learning into their day to day operations. But the other part too, right is the fact that everyone that you will be interacting with as a data scientist is most likely coined to be a non data professional, right? They're going to be a marketing or sales or product. What they really want to hear is essentially like, how are you going to help me solve my problem? You know, the most common feedback that I've gotten in interviews for myself that's a positive, uh, been menopause of feedback has been something along the lines of like, you know, Oh my God, you talk like a regular person that actually a couple people said, like they did a couple of interviews just straight up said that to me like in, in some of my interviews where they're like, Oh, Oh my God, this is like so refreshing. 

Speaker 2: (20:11)
We're finally talking to someone who's like not a robot. Um, you know, so being able to think like a business product owner, um, and being able to understand what is the strategic value, um, you know, what part of the business, uh, are you pushing the needle for with your work is, is such a key skill. And that's honestly, I think that's a skill that's really taken me through all my different career jobs. Um, whether they've been data science or data analytics or, um, you know, non data. That's really, it's a key skill. Just not being a parrot, you know, just parroting random facts about algorithms and 

Speaker 1: (20:48)
knowing them off the top of your head or just knowing how to optimize some algorithm like that stuffs whatever people can look that up online. And, and that's that, right? I mean, it's really taking a kind of an artist approach to your work and being able to see the whole picture and then communicating that back to your stakeholders, right? The people who are actually going to be using your products. 

Speaker 2: (21:09)
Yeah, absolutely. And a huge part of it is, you know, knowing your audience. So for example, um, like if we consider, um, you Pablo Picasso, he wasn't, it's arguable whether he was creating his art for everyone. Right? There is, there is a segment of the audience, um, that when he was painting or when he was creating art, um, he was, he was definitely thinking about, about a certain segment of the audience. I'm same way with my ne, you know, um, it's knowing your audience and being able to speak to that segment of the audience. Um, and, you know, change your communication so that it's, it's respectful and it's treating people on an equal level. Um, it's, it's, it's so important. It really is. And also too, you know, it's, when we talk about strategic value, I think sometimes they assigned to us, we'll kind of get lost in their models. 

Speaker 2: (22:02)
They'll get lost and like, Oh, how beautiful or elegant it is. Um, so if we take for example, like a cattle competition, it's not a perfect imitation of what working as a date as a data scientist looks like in your day to day sort of situation. But it's pretty close. Cause you know, with like when a cow competition, right, you're given data, you're given us a task and it's who can, who can, um, you know, accomplish the outcome of the task the best in like the, the two months or the one month or the four months that you might have, um, the cattle competition's not grading you on like how elegant your code. Elegant code is. A tool to help you, for example, iterate through your models to help evaluate, to help automate a feature engineering. But a Cal competition, they're like, we care, you know, w who gives us the best result in the short amount of time essentially. 

Speaker 2: (22:53)
Um, and that's kind of how I think. I think to some degree that's how it is. I just should kind of approach, um, their projects and, and you know, a lot of them will not, it's the, you know, what is the outcome that this project is supposed to help? Is it, it's supposed to increase sales as opposed supposed to, you know, cut down churn, improve retention. Um, is it to, um, you know, make the user experience delightful. You know, in the case of like Google, right? Sometimes like using a Google product that's incredibly delightful. It's seamless. Is it meant to facilitate sort of that aspect of the business and, and really asking yourself, you know, is the project that you're working on? Does it like really help push the business? If not, then you know, that's just kind of a day scientist thinking in the corner, you know, and their own will like private workshop. Um, but if it does push business value, then that's where, um, that's where the day scientists becomes a, a really kind of a powerful voice at the seat of the table.. 

Speaker 1: (23:55)
Nice. I like that touched  on being a growth hacker and a data scientist focused on growth, can you just kind of define what that means for our audience? 
Speaker 2:
Yeah, absolutely. And, and so, you know, that after sort of a, uh, of a growth hacker is, you know, it's similar to a day. I just live in the sense that a lot of people have, I have a million different definitions for the way I describe it. Um, but in terms of, um, the, the simplest one I can give really is, uh, a growth hacker is focused on growth. Um, and what that means is essentially, um, scaling the acquisition and retention of users, um, through, you know, various methods, um, to, uh, you know, essentially like create a system one way. Okay. So I can kind of describe this better through examples, right. So when we think of a typical sort of sales and marketing situation or a funnel for most companies, we do think of it as a one way funnel, right? 

Speaker 2: (24:52)
We think like, okay, marketing goes out, they've lost a bunch of emails and then they get a bunch of leads. Those leads get into the pipeline. Um, you know, some sales reps will go through, they'll pick the best ones and then they'll work on that deal. And instead of, you know, growth systems are all about how do we, how do we create these loops where it's in terms of a growth loop, what it should sort of cover is uh, acquisition, you know, monetization, uh, engagement and retention, right? And it just becomes this loop and that's creating these, the system of loops such that, you know, people are constantly starving, engaging with your business. They're constantly adding value, constantly arriving value. And through that you essentially create growth, not just in terms of getting new users, right, who maybe come to your company cause they may read some content marketing or because they were referred through like a Twitter link. 

Speaker 2: (25:47)
They come to your company and then they continue, you know, adding value through their own content, through their own engagement. Maybe if you're, for example, a gaming company, this person, um, takes like a, um, they take a video of their game plays, they post it, you know, so that's what, uh, uh, growth hackers focus on. Growth hacker is focus on finding, I'm using programmatic methods, um, using, uh, insight about, you know, how digital marketing and the different kind of marketing and sales channels work and creating systems for that to continue delivering value to the business. Um, another way, I guess the thing about growth is that people said that this was this, this growth is what, um, advertising agencies should have been focused on 20 years ago. Um, right. And they're essentially using data to make a law these decisions, um, you know, and to constantly grow the business and that, that's, that's really focus of growth. 

Speaker 2: (26:43)
So, you know, as a growth data scientist, what that actually means, right? Is that I work with the growth, the, the growth team. Um, and I do a couple of things. One thing I do is I provide them insight into, um, not mostly just how their campaigns are going. Because growth marketers typically do tend to be a little bit more technical. I'd say. I was like the sales and marketing crowd. Um, but giving them insight into how their campaigns tie into like user engagement, helping them, um, programmatically segment users, helping to forecast leads are coming in. Um, you know, even for example, uh, coming out with machine learning models to do a couple of things, right, like predict who's going to like churn out or who, who are, who we're going to have issues retaining. Um, it could be recommending specific content, you know, so getting people like more engaged, um, or, or keeping them engaged like in our product and services. Um, it could be, you know, it could be even, for example, understanding if they're potentially at risk for getting COVID.

Speaker 1: (27:44)
Thank you so much for that. I appreciate that. Kind of shifting gears a little bit here. I remember reading about your lifelong mentor and tents and coach Alfred in your medium story, which by the way, uh, if you guys haven't checked it out before, make sure you check out Mickey goes a series of posts on how she broke into data science, but the advice he gave you when you're going through some rough times, um, can you share some of that advice with our listeners and how that's helped you in your journey to becoming a data scientist? 

Speaker 2: (28:11)
Yeah, absolutely. So, you know, even more context. Um, so I've known Alfred, Oh man, how many years has it been? So he was one of my, he's my fencing coach in high school. Um, so if we do the do the math right, I've known him since 2004. Man, that's 16 years. Yeah. Lifelong mentor. I call him uncle Alfred. He's, he more than any more than you want. Um, if I know something is serious with someone I'm seeing, he is the last stop they need to get his approval before things go forward. Um, my parents are like second in that list. He's, he's the final stop. I have a huge regard for him. Um, so, you know, the advice he gave me. Um, yeah. So when I was first working at the hair salon and going back to that feeling of, Oh my God, what am I doing with my life? 

Speaker 2: (28:58)
Did my parents really just pay for my college degree for me to sweep a pair and you know, like to have my hands dyed purple from all the color correction shampoo. I was definitely, I felt like I was in a really low place. Even when I finally was able to move out, uh, my parents' place and find my own apartment. Um, it felt like it should have been a victory, but it was sad and I was like, now what did I do in my life? He, you know, he would say to me like, you're going to look back at this time and you're going to realize how valuable the lessons you learned, where we really talked to me about how there's humility and taking pride in the work that you do and the level of, of quality you provide air, regardless of what that job is. 

Speaker 2: (29:43)
Cal Newport, he, he writes about this in a book called so good, they can't ignore you. And that was essentially offers advice, which is I, you know, if you're going to be, if you're gonna work as a front desk girl at a hair salon, you bird you or be like the, the best front desk girl at a hair salon. You, if you're gonna sweep hair, he's like, you better be the best hair sweeper. You know? And he, and he learned all of these lessons because, um, you know, when he grew up in San Francisco, so his parents were farmers as part co-op and so he would go help them, um, sell at different farmer's markets, you know, so that's how he learned sales marketing, you know, and he would help out his family. And, and he, he also had that experience too. You know, where in the U S server service workers are not, they're not always given the kind of respect and dignity they serve, deserve as valuable contributors to our society. 

Speaker 2: (30:33)
Um, and so that was like, those were the lessons that he really helped teach me was that it was the feeling pride in the work that you're doing. Um, really kind of believing in yourself and also making sure that every day, you know, you take steps towards that goal. Now he's an incredibly last actually 20 years at least you went to college. He dual majored in mechanical and I think, um, structural engineering. Now he's an incredibly successful engineering consultant. His clients include, um, you know, Google genetically ***** Apple, um, you know, huge, huge clients. Um, has a beautiful house in San Francisco, uh, is raising two amazing boys with his wife. Um, you know, he's got this really kind of enviable amazing life and it, it helps taking that advice from him because I know that he worked at it like day in, day out. It was hard work. 

Speaker 2: (31:27)
It was time and effort, but it was also like believing in yourself, you know, because I think something that I did experience at the Harris lawn, which, which really drove me to go into the startup world essentially being kind of mistreated, you know, so people would come in and they would sort of treat me as if I was stupid or they would definitely say like really kind of mean things when like the hairstylist weren't around, you know, they would make fun of me. Um, even like, I remember there was this one client who came in, he was number 40 on I think Forbes Midas list in case anyone isn't aware. The, the Midas list includes essentially all the wealthiest multimillionaires in the U S and also internationally who have the Midas touch. Everything they touch turns to gold, right? Early investors of Salesforce, of Google, of Facebook, of square Twitter, you know, so, and I remember he came into the salon and the *** was like, you don't even get to go near him. 

Speaker 2: (32:20)
You don't say anything to him. You don't ask them questions. And I remember like experience, like going like, what? Really? Like I'm not like the bad person or anything. Um, now, now I look at it as like, okay, you, you probably was just like tired and stressed out and maybe he just wants to chill time in the salon. But you know, those experiences were really formative and that was a huge part of when he, when I would look at these people and I would be like, what is the risk between you and me? Like, really, it's not just like clothes, it's not money, but like how, how do I get from like behind the table to now being a client at one of the salons? And Alfred really helped me with that. 

Speaker 1: (32:55)
Yeah, man, that's awesome advice. Especially with what he said about just be the best at what you're doing right. Like you need to treasure what it means to do a day's work. Work is really a chance for you to, to in any capacity, whether you think it sits big or not, it really is your chance to do some type of art, to create a gift, to do something that matters, even if it's just changing the fluids in the, in a shampoo bottle or, or sleeping hair. It's, it's your opportunity to really express yourself in any way. So I thought that was really, really good advice. 

Speaker 2: (33:25)
Yeah, it's a, it's about having that cross in mindset. You know, it's about picking the skills that you need to work on and working on them every day. Um, but it's also acknowledging that, you know, changes. This is totally possible, you know, and that was the other kind of advice and, and, you know, sort of the things that you told me is he, he would, he would say like, look, you know, make here, your future is not defined by like this moment today. You know? So, uh, one of my favorite, favorite books is a, uh, a weightlifting book. Um, uh, you know, that talks about, it's like an intro to the starting strength program.But what's great is that, I mean the introduction, it talks about how, you know, a workout, the importance of the workout is not that workout itself. It is the workout and it's place in a, in a chain of workouts, in a, in a sequence of workouts that eventually leads you to have this amazing like body and physique. 

Speaker 2: (34:26)
And it's the same thing with work, right? Just because I, you know, in that sequence of events, you know, I worked as a front desk girl at a hair salon, not even the stylist, right? Front desk girl. Um, it, there was nothing to say, right, that I could not eventually become a data scientist. Right. It was, it was one job, it was one day, but you do something a little bit every day. It compounds over time. And if you do it intentionally with this, with this craftsman mindset, um, you know, eventually you will like reach your goals. 

Speaker 1: (34:57)
Yeah. Robin Sharma has this saying that I love, I'm going to butcher the quote here, but it's something along the effects of small, seemingly insignificant daily efforts for compound into huge results in the long term. And I think that really does embody that philosophy there. One of the things that I respect most about you is your commitment to helping aspiring data scientists navigate the process of upscaling, um, and the landscape of the job search. So tell me a little bit about where that desire comes from and what's the number one actionable tip you can share with our listeners with respect up-skilling., 

Speaker 5: (35:38)
Yeah absolutely. So,um, you know, my, my, my desire to like help aspiring data scientists comes from, um, you know, my experience of, of like really needing help. So, um, realistically my serious foray into data science as a data scientists, uh, started about two years ago, I would say, you know, when I was first starting off as that hybrid, um, you know, day side to see analyst role at Autodesk maybe, maybe. Yeah. Two, two years ago, I was working on different projects at work and, but you know, I was running into a lot of difficulties. You know, I tried building a customer scoring model and I ended up having to do some kind of mix of uh, you know, [inaudible] rules versus like some automation, but even things, for example, when we are trying to segment users, when we are trying to break, you know, which users we should go after to help sort of an advantage, analyze the product. 

Speaker 2: (36:30)
I was struggling a lot. I just did not have the tools to be successful at that point. I was still literally coding everything in Python or in R. I only learned Python out a year ago, you know, so I need a lot of help and you know, in order to make that transition, um, I enrolled in a bootcamp on springboard. I did that for eight months to upskilled. We're on projects, graduated still was having, uh, no job offers like had a really terrible time finding a job. Um, joined the STJ, you know, that was, you know, that's where I met you and Kyle and Chris and jeans fashion, um, for four months and then got a job at job offer afterwards because DSJ just helped me so much in terms of the actual like job search process, but it was like still a struggle for a year. And I also see all these people to online, they think they need a master's, your PhD to be successful in the field. 

Speaker 2: (37:23)
And you know, right now I would say like a lot of this science and business analytics programs are still fairly immature. You know, they're not quite at the point yet where they really will deliver value for the money. So, you know, I see, I see a lot of students there, um, a lot of potential candidates, they, the Hubble's miss miss misinformation, like flying around. They're tossing money at these different degree programs. I really want to help people out because like I, I was there, I was so lucky to have some great mentors in my life and not everyone else's. And so being able to, um, even if it's tell my story where it's like, Hey, like I don't have a master's or a PhD, but I did it. It took, it took great and some elbow grease, but I did it, you know, or for example, putting together workshops. 

Speaker 2: (38:10)
Um, you know, I'm working on right now, I'm working on putting together two workshops for the width conference house was happened, may one of them is on, uh, you know, growth marketing decision science. Uh, the other one's on visualization. You know, all that is, is hopefully to help empower, um, data scientists from like nontraditional backgrounds to consider jumping in. Um, you know, what, in terms of one of the ways that they can do that, I would really say as much as possible, you know, they should try hacking the learning at first. There's lots of different resources, but at some point I think they should also make the make, make the decision to consider joining a program like these. TJ, I'm more springboard because it helps to have a community and a structure, um, to really kind of empower and, uh, speed your progress forward. You know, and so kind of like thrashing around and trying to chase, you know, one tutorial after another. There's, there's a lot of noise in the data science space. 

Speaker 1: (39:08)
Oh yeah. Tremendous. Tremendous 
Speeaker 1
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Speaker 1
Uh, but one thing that I, I've heard you talk about was this concept of mentors at a distance. Uh, who would you say is your favorite mentor at a distance, and what's an actionable tip you learn from them that you think would help benefit, um, an aspiring data scientist or maybe even a data scientist's shoes already in the field.
Speaker 2
 You know, my top three mentors I distance are definitely like Cal Newport, uh, ramen study, uh, for people who don't know around the study. He, he wrote the, I will teach you to be rich book and he has a ton of offerings around that. Um, you know, and also Meg Jay, um, so she wrote this book, uh, how it's basically like how the, the, the defining decade, um, how the thirties are now can mean twenties. Um, so you know, these mentors that had distance, um, you just learned so much from them, um, from Cal Newport specifically as it relates to the eScience, that principle of so good. They can't ignore you. You know, I, I'd say that that's incredibly important. Um, is simply things that my partner [inaudible] is a, he's a director of advertising and creative design for walmart.com e-commerce. 

Speaker 2: (40:58)
Um, and we actually always have this like pseudo argument about passion versus, um, you know, passion versus, uh, skills or a grit mindset, you know, um, and so good. They can't ignore your mindset. Yeah. And for him, passion, especially in his line of work. I w I would say like passion is more in for everyone, but for him, you know, his belief is that you need to be passionate about something in order for you to go and make a career out of it. Um, for me, I take the Cal Newport approach where you, um, you find passionate in your career from getting really good at what you do and when you get really good at what you do, that opens up, um, the possibilities in terms of, you know, freedom of, of where creativity of work and being paid what you deserve. You know, and a huge part I think of how you embody that craftsman mindset is, you know, you strategically understand what are the areas that you should be working on. 

Speaker 2: (42:02)
Ideally you would apply the, you know, the Prieto like 80, 20 [inaudible], right? Which is that like, what is that 20% that's going to dry that 80% value? Like really understand what it is you need to work on. And then you set up a plan, you work on it, right? You maybe need to get like an instruction or a tutorial, but you do so very like strategically [inaudible]. And then once you kind of learn that skill, then you work on the next skill that you need to, you need to learn, right. Really successful data scientists, they're not always doing the things they're great at, like really successful data scientists. Um, there are things that, you know, they encounter in their job. Maybe, for example, they need to create some, some kind of model or they need to use some kind of algorithm or some high tool set. Maybe they've never used TensorFlow, but it's kind of required by the product, by the business, and they go on, they learn it, you know, they're not always sticking with where they're comfortable there. They're constantly pushing the boundaries of comfort because that's essentially how you just become better, you know? 

Speaker 1: (43:07)
Oh, absolutely. Love it, man. Like I actually, I am now in the camp of the Cal Newport. So good. They can't ignore you view of passion. Um, but before I'd encountered that book, I was, I kind of fell into that natural passion type of thing. Right. Like, Oh, you got to find your passion and then pursue it. But then when you take that kind of standpoint, you're operating out of a mentality that every human being has a passion from the get go right from, from, from birth. Um, which I think kind of is a fixed minded kind of point of view, right? Because passion can be developed and it can be cultivated and you really, truly uncover and discover this passion if you're persistently working on something. Right? Because if you're not persistently working on something that obviously it's not something you're passionate about.

Speaker 2: (43:58)
No, no, absolutely. Absolutely. Yeah. You know, and, and when I was in college, there was a lot of pressure I felt, um, because I was hearing this like, you should find things that you should be passionate about. So in college, I, I was passionate about monkeys. Then when I look at realistically what the prospects were, it was either go into zoology, go into some kind of anthropological research that wasn't really super interesting to me. But at the same time I felt like I, I, I felt like I had no direction or or rudder. I had no sale because if everything, if you could make a career out of everything, then what was really meaningful, you know, but by instead like limiting the conversation to or the construction, the conversation to okay, what is this next thing you would like to do and what are the next steps you need to do to get there? 

Speaker 2: (44:46)
I think is a lot more productive than fruitful than say like, okay, like what is this thing that you are going to love in 10 years? You're not really sure. Like I've definitely like I've, I've had hobbies and jobs and and friends like like just totally switch out in that time like multiple times over in 10 years. So I, to me it seems like unrealistic to go like okay well like this is a career and this life you're going to have for the next 10 years. Even right now for example, a lot of people thought they were going to get married or they thought they were going to have their job offer. This year and like that just didn't happen, you know? So I'd say like, okay, so actually one thing I would say though is that when data scientists are structuring their learning, I think this is important, right? 

Speaker 2: (45:25)
This concept of adaptability, it's so important because I think I love putting into other learning plans and study plans, right? And I love playing things out for the next six months. But you know, when you go into interviews as a data scientist sometimes like things will come up. Like for example, you'll constantly eat. You might think that, okay, you're bad at building machine learning models, but actually like your area of weakness is a external design testing that happened to me. I, you know, I would go into interviews, I would get some really obscure questions too, just because for whatever reason, you know, senior day scientists with PhDs with like 20 years of experience law of asking the super OCR questions of like, can you describe the implementation of this algorithm without a whiteboard or any resources? Like, no, I can't because it's not relevant to my daily work. 

Speaker 2: (46:10)
Right? But yeah, I was going into interviews and I would get questions a lot on like experimental design testing and analysis. And I, that would be, those would be the kinds of questions that I would strike out on. You know, it was crazy. I was like, okay, so maybe my machine learning and programming is not actually what's holding me back. So at some point I had to adapt my, you know, study plan to incorporate a lot more in, you know, external design and testing analysis. And the funny thing is that that's now like a third of the work that I do right now. They thought my knowledge is good enough to hire me. Yeah. To kind of incorporate, include that. But that was, that was, that was me being adaptable and to some degree, right? Like you can make, you can make these plans, but you always have to adapt and find another way to accomplish your outcome. 

Speaker 2: (46:51)
And that's, that was something that, um, I actually learned you're taking a bunch of problem all classes, all enough, you know? So for people who I guess aren't aware, Chrome, gauzes, Israeli martial art and the teacher I had for a few of those classes, he would emphasize, he would give us this task, right? Like do as many pushups as you can for two minutes. And essentially, like he wasn't saying that you had to do full-on body push-ups, but he was like, find a way to do as many pushups as you can, but never do a full stop. It could be you're on your knees and you do like a half push up or you know, could be you take breaks, but he's like, never do a full stop. Always find a way to adapt and to accomplish the outcome that you need to, you know. Um, and so I think that's like incredibly important in the mindset of a successful data scientist. 

Speaker 1: (07:35)
That's a very, very powerful, even in the pursuit of a goal, you're going to come through setbacks, but you need to just find a path to, yes, I absolutely love that. Wow. Um, so, so let's talk about being a specialist to purses being a generalist. Um, could you give your definition of both terms and share some advice with aspiring data scientists or even data scientists who are in the field on picking which path would be best for them? 

Speaker 2: (48:00)
Yeah, absolutely. So, um, at least the way I kind of define it, uh, you know, a generalist versus like a specialist with a general state of scientists, you're expected to have a pretty like bra. So we think of it as not necessarily like a T shape skill but a w shaped skillset. Right? And so, um, you're just for context for people who don't understand this, so when people say they like a T shaped skillset from an individual, what they're saying is that like an upside down T they want like sufficient experience in like a broad set of skills, but for someone to be very deep in like one set. So maybe for example, a data scientists, um, they have like the necessary amount of skills in, in SQL and like getting data from API APIs and web scraping and building models and building like reports and dashboards and like some like automation, but maybe like what they're like really amazing at is, uh, data visualization to the point that they're in artists or maybe what they're really, really great as they have deep knowledge of NLP or maybe have like spatial data. 

Speaker 2: (49:04)
Right? Um, so when you're in general state assigned is, is actually more like, it's a, it's like a squash w you know, you need to be really good at getting data first off. Then you need to be really good at analyzing data through like exploratory analysis visualizations through like descriptive measures. And then you need to be really good at you, pretty good at doing something with it. It could be servicing it into a report dashboard or it could be building a classical machine learning model. Right? So that's like a general data scientist thinking they're, they're kind of, um, like a Swiss army knife, but especially state aside. And they're usually, they usually tend to be a little bit more senior because they found this, this niche or this area be a scientist be assigned so that they're really, I'm really great at or that they spent significant time exploring. 

Speaker 2: (49:47)
Um, they'll still have that kind of like, they're close to the T shape where they still have a broad base of skills. But essentially like if you, for example, um, if you go into like a data science, like, like more room or in the office, right? And you're thinking to yourself like, okay, I have, um, I have a bunch of like customer support emails or feedback. I really need to understand, um, you know, what are the, what are the top things that people are talking about? And you, you're like, I need to, I need someone to help figure that out. You might go like, Oh, I know this person has worked with a lot of tech stated before and they'll understand that like, Oh, you want essentially to accomplish topic modeling and I know how to do that for you. Or maybe, for example, I'm like, so at Lovango, um, we do have, we have people that do like generalists work. Um, and then we also have people on the ML research side that are specialists, you know, so one of them I'll researchers, um, you know, he has this like really deep experience in productionalizing and launching recommendation systems, um, for health companies. Right? And that's like a very kind of specific sort of area he has, he uses some of the same tools, but how he uses those tools to solve a problem, I think is what makes him, um, a specialist and also a very senior machine learning researcher. 

Speaker 1: (51:04)
It's that kind of like that notion of, um, foxes versus hedgehogs. I forgot which book that was mentioned. It might've been from good to great. Maybe the hedgehog knows one thing really, really well, but the Fox knows a little bit about everything. Kind of shifting gears here now, I was wondering if he could speak to your experience being a woman in tech and if you have any advice or words of encouragement for our listeners. 

Speaker 2: (51:24)
Being a woman in tech, especially, uh, you know, uh, a female, uh, of color, Asian female. I would say that there are some great points. And then there are some low points. Some of the great points is when you meet like a community of individuals who are, you know, also like women, women of color in tech or even just like allies. Um, you know, a lot of my, a lot of my favorite people in tech are men, straight men or gay men or you know, um, trans men, um, like are allies. And you know, when you meet those people, it's, it's a fantastic experience. Like I, I would not change necessarily a lot of it, but there are definitely some low points, you know, and some of those low points include, for example, um, like being harassed that were, or, you know, being harassed online on LinkedIn can be, it can be a really amazing platform. 

Speaker 2: (52:18)
It can also be a platform that you know, engages and encourages in a lot of toxic behavior towards women, especially if they are, um, you know, outspoken and especially if they're considered a beautiful, you know, I, I had an incident happen recently on LinkedIn, um, this wouldn't be the first time and it's definitely not the worst experience I've heard of on LinkedIn. But you know, where I had some individual like messaged me and basically go like, Hey, beautiful, I want to get to know you. And the interesting thing was that, eh, you know, I, I, so I posted that, that message thread and I was just, I, there was something, uh, that about that day where I was just, I was on fire. I was, I was so mad, you know, and I responded to them. I, and I just said like, look, you know, first off, this is incredibly unprofessional behavior, I'm enough for your daughter and thoroughly like in this kind of behavior messaging, it really drives like women and a women of color, like away from the platform. 

Speaker 2: (53:19)
Because essentially like we want what everyone wants, which is, you know, you go into work, you engage in, it's a safe space and you work on like cool shit, you don't necessarily need like your gender, your race or your age brought into it. Um, I don't go in to work and ask people their political opinions. I don't ask the religious like opinions and in affiliations because it's totally irrelevant, totally irrelevant. So, you know, so that happened and I posted and there was definitely like a lot of positive feedback all enough. I was then messaged by um, some, some pretty senior leaders in the tech industry who, and this was like CFOs or CTOs that were like, well why, why was his behavior inappropriate? Like why can't you be beautiful and smart at the same time? And I'm like, okay, so if that's the kind of attitude that's going on at like the senior leadership of your company like that you are embodying. 

Speaker 2: (54:10)
And I, and I had to educate them a little, you know, so you get instances like that. And I would say like, my advice to like women in tech is first off, you know, like, you know, find your allies, find your community. And secondly, I would also say like, w also kind of stand up for yourself and that's all. And I don't want to shame victims, right? As someone who is, is, is a survivor of, you know, assault and harassment and stalking. When I say like stand up for yourself, I underst I recognize that there are situations where someone can't wear or you fight back and, and things still happen. Like I, I totally feel that I, I recognize it, but when it comes to, I think interactions on LinkedIn or in the professional setting, I would say if at all possible, like definitely stand up, like make your voice heard because it just helps everyone around you. 

Speaker 2: (55:00)
And then, you know, my, my third advice for, um, for women in tech also is at to, not to not assume, say, be worried about bringing, bringing their whole selves to work. So I talked to a lot of women were a women in tech and I'm a part of a bunch of um, like women in tech, uh, career groups and you see these strides, these like, you know, conversation threads where women are really worried about like, okay, if I dress up or if I, you know, like they'll dress down for work because they don't want to like emphasize their looks or you know, they'll speak a certain way or anything like that. And you know, my advice would still be actually like feel free to like bring your whole self to work. Feel free to bring your whole self to your work as a data scientist especially, it's not all about the skills you list on your resume. 

Speaker 2: (55:45)
It's not about knowing five or 10 different programming languages. It's not about knowing like five or 10 different algorithms or anything like that. But it's about bringing your unique perspective, your unique storytelling, your unique experience to the table, you know, to the analysis to how you think about these problems. And so that, that really is, you know, I think when, when women, I think when women, when we sort of censor ourselves, we're not really giving the best we can to our work, into the people around us. When we sort of censor ourselves, we're not really giving the best we can to our work, into the people around us. And that's not to say that we shouldn't be respectful, right? Everyone should be respectful to each other. Everyone should engage in respectful communication, um, but they shouldn't be afraid to, um, you know, to be their most professional selves. And also I would say, you know, sometimes if you're in an environment that doesn't encourage that, it's the environment. It's not you. 

Speaker 1: (56:43)
Awesome advice. Thank you so much for sharing that. And I'm sorry you had to go through that horrible situation. On the flip side though, you've been really encouraging and empowering, um, and especially as a member of widths. Um, I was wondering if you could talk to us a little bit about the recent accomplishment you've and what the experience is like
Speaker 2

Yeah, absolutely. So, um, recently I'd say about maybe last month. Um, so with, uh, women in day science, it's an organization, um, was start out Stanford I believe. Um, and their goal is to serve as a community of, um, allies, uh, to give support and to encourage more women to come into the tech, into the data science field. Um, so, you know, about, I'd say a month ago they ran a one two month long, um, data thorn on Kaggle, um, and involved using MIT's ghosts dataset now, um, the ghosts, this, uh, initiative from MIT. So their goal is to aggregate ICU mortality, uh, information from all around the world. So then try to come up with some kind of, um, generalizable risk model for patients that are coming into the ICU. Um, and so they, you know, they really stay to set for, uh, for this specific task of essentially like, can you come up with a model to predict, um, ICU mortality, you know, and there was a lot of challenges with this particular dataset. 

Speaker 2: (58:16)
So one of the big challenges, right, is ideally, uh, mortality is a very imbalanced classification problem, right? So in that sense of we would hope that, you know, of 20 people that are coming into the ICU, not more than one of them are, are dying, right? Like, ideally we would hope that it's incredibly imbalanced dataset. Um, the other, you know, the second challenge is also, um, given that this data was sourced from, was sourced worldwide, we would have hypothetically expect to see, um, you know, some differences in mortality from people who come from ICU and say like the Asia Pacific versus people who come from ICU in Latin America, we would expect to see differences in terms of like the population. Um, you know, so something that's like a big topic right now and, um, you know, digital health and medicine is, is a social determinants of health. 

Speaker 2: (59:13)
And there's this idea that I think most people, until we understand, right, that, you know, you know, where you grow up. Like what part of the socioeconomic strata, you're a member of, um, your geography, um, your education level, all these things will impact your health outcomes. Um, so you know, that's so the challenges of like imbalances that, you know, they come from different populations. For example, you know, something that we re we realized, uh, bit later in the competition was the way the train test split was along ICU. So hypothetically, you know, you are creating a, a trained model on this trained data set, which could have potentially come from one set of hospitals and then you have to test on another set of hospitals which potentially have, you know, a different population. So there was also some drift to manage, you know, but it was a, it was an incredibly challenging, um, incredibly interesting challenge. 

Speaker 2: (1:00:11)
Um, it was myself and uh, Michelle is, and Susan  and Susan are Steven strips of the STJ. Um, Susan recently got a job offer, which is great. Right before COVID hit, Michelle was a friend of Susan's and was invited into it. So the four of us, we, we worked on creating a different sets of models for two months and it was a really amazing experience. And for everyone to, it was actually their first time ever approaching a cow, a competition. I think. I think there's some fear, right, where people are kinda like, okay, well, you know, we're worried we're not going to be able to do well or uh, you know, I think there's some reluctance to kind of jump into the fray. And so this, this would say athon was actually facility set up. Um, so that, uh, to encourage female perspiration and also hopefully like new participation. 

Speaker 2?: (1:01:00)
So for example, teams had to be 50% female representation. Max size was four, you know, and so it was a great experience, but it was, it was also really challenging, you know, because I think the common question I get from students is, um, how, how much time are you typically given to like turn around and produce model and industry? Um, so if it's a simple model, it's usually like two weeks and it's, that's like a really like, rough cut, right? Like if you're doing a regression or or whatnot, sometimes it's even in a matter of days, but for like a fully robust, you know, you, you test out different models and you engineer features for us that we got shoe months for that. And then if you actually look at how much time people are able to meet, um, we had to do quite a bit of like asynchronous, um, um, model development and feature engineering evaluation. 

Speaker 2: (1:01:54)
And also during the meantime too, um, I think each of us, we were doing some kind of tutorials or classes to kind of like get up to speed to like how you produce a model and like cattle competitions. The Coursera has I think, uh, this really great class, which is like how to win a day, a science, how to win like a data science cattle competition or how to win in cow competition. That was incredibly important, um, useful in our endeavor. And you know what was really nice was that, so the outcome was, even though we were not top five, um, we got the top 80% of the competition, which was about, uh, it was about 900 teams. So that was like 82nd, 83rd place around maybe 79th place or something. That was pretty impressive. Yeah. Cause if you look at all like people above us, like we were, we were hoping for top five cause it's top five, he gets a nice prizes. 

Speaker 2: (1:02:46)
But you know, when we were looking at all the 18 support us, like most of them had at least like two, like if it was they had a bunch of solo teams. Right. And he also had a bunch of, uh, non-solar teams, but most of those teams had, had apparently had significant experience, um, doing Kaggle competitions. So if some of them were close to actually grand masters in, um, cow competing. So, you know, I'm, I'm really proud of the way the team did. Um, and a saw and, and Michelle and Susan, like each model they produce, like really helped push up our AUC violin. You know, by like two to three percentage points, which, which was fantastic. I'm so really proud of, of, of how they did. 

Speaker 1: (1:03:29)
Yes. That's an awesome result, man. Great. Where it guys, if anybody wanted to check that a workout that you did, is that available online? 

Speaker 2: (1:03:36)
Yeah, we're, we're, we're cleaning it up and we're gonna we're gonna put it on get home so that way people can come check out our work. 

Speaker 1: (1:03:42)
That's awesome. So one last question. You jump into a real quick lightning round. What's the one thing you want people to learn from your story? 

Speaker 2: (1:03:50)
You know, just do it , right? Just do it. Um, yeah, I mean that really, that, that is the biggest thing, right? Is that I think if you, if you look at my background, you'll get at my story. Um, you know, and kind of where I came from seven years ago, it was a, it was an iterative journey. Um, seven years ago I didn't really know data science, machine learnings. I said maybe that knowledge came into play like three years ago, but you know, what I did do was I just tried to make the best of every situation. Um, I was always like working hard. Um, it really, I mean like in that, in that kind of philosophy like applies even to like my personal life, you know, whether it's working out studying or like spending time with family or even like, you know, in my relationship, right? It's, I always want to be like the hardest working person in the room because at least then like I can feel satisfied with what happens. You know, at the end you control what you can, but the thing you can control absolutely is like your grit and determination, you know, and not making excuses for yourself. Like, look, if I could do it, I really don't see why other people couldn't. 

Speaker 1: (1:04:55)
That is an awesome lesson as well to learn from your story. So let's go ahead and jump into lightning round. So R or Python. 
Speaker 2
Python.
Speaker 1
What's your favorite question to ask an interviewee? 

Speaker 2: (1:05:07)
Oh yeah. So, um, what I always like to do is we have that list of like company values. Where are we? Do you know, how did you embody company value a or, or B? I actually like to ask people, you know, what's a time where you didn't embody company values C and how did you grow from it? It's a great question because honestly like people get really stuck any kind of quickly see like how they take accountability for their own actions. So for example, if it's like, you know, I'm, I, I didn't do well on this like team project and you know, it's cause the other team members like didn't kind of pitch in. But the way I recovered from it is I like did a hero maneuver. Um, usually I, that's to me it's a pretty good sign that like maybe they're, they're not someone melting accountability for stuff. So that's my favorite question is like, at what point in your life did you fail at, at embodying embodying this value? 

Speaker 1: (1:05:59)
That's really, really good. Um, because that goes to show that it's not only about the technical skills that you need to prepare for for an interview, it's also taking fact or looking at the facts of the company, going to the website, understanding their culture, understanding what they value and that mission statement and familiarizing yourself with that. What's the weirdest question that you've been asked in an interview? 

Speaker 2: (1:06:22)
What animal would you be? And this one was really weird because, um, that was the first time I ever entered the first time I editor ever interacted with that question or ever got it was when I was, uh, in college and I was interviewing to be like a lab assistant to help dissect frogs. And so the lab manager like asked me this and they're like, so what am, what would you be? And I wasn't thinking so quickly blurred out like cats because they're loyal and they're friendly, which obviously is actually not traits of, of what cats are. Um, they just simply like not always loyal and friendly. And, and his response was, Oh, you know, that's so funny because my sister would have said cats because they're like lazy and fat. And that was like mortified. I was like, Oh God, I still got the job. But like, that was the weirdest question ever. 

Speaker 1: (1:07:11)
That's hilarious. Actually, it's funny because somebody had one of the interviewers that I, that I've spoken to had a very similar question and it was what would be your spirit animal? So that's interesting. So, and she also said cat. I was wondering, Oh, what's your favorite fiction book? 

Speaker 2: (1:07:31)
Oh yeah, that's a good question. Um, so my favorite fiction book. Oh man. Okay. That one's really hard. I would say, uh, though more recently I, I've been actually reading the Witcher series. It's super good people. If you didn't know 'em yes, I know you're, you know, the Witcher, Netflix show, the Witcher games. But the games are actually based off series of books. Um, I would highly recommend people read it. There's seven of them. 

Speaker 1: (1:07:57)
All right. How about, how about favorite nonfiction book? 

Speaker 2: (1:08:01)
Um, yeah, favorite nonfiction book would be, uh, Meg. Jay's a the defined decade. How the theories are not the new twenties, um, super polarizing people, 20 and below who read it, love it. People 30 involved tend to hate it because, you know, it makes them kind of feel like, Oh my God, what did I do with my life? It's a fantastic book. And basically what she writes about is a really kind of like how there are certain sort of milestones in a person's life that you know, are still sort of immutable in some ways. Um, but that also now's also the first place where I encountered the, um, you know, skills, not passion, argument. It's a fantastic book. Um, that and also, um, you know, rom, it's like I will teach you to be rich, fantastic book on personal finance. Uh, you know, everyone could kinda use a helping hand there. Um, so I think people should definitely check those out. 

Speaker 1: (1:08:54)
If we could somehow get like a magic telephone that allows you to get in touch with a 20 year old Mikiko, what would you tell her? 

Speaker 2: (1:09:02)
Oh man. I would tell her like, everything is going to be fine. Seriously. Like, don't, don't, don't freak out so much. I, okay. Well one thing I would tell her is, is don't continue dating dating that guy like did get out of there. Um, go, go have some fun in college. Um, so, and then the second thing I would say actually is, uh, I would have told her to not drop and throw Nick like to keep doing Anthony con. I would always tell her to take some like intro programming classes and have a little bit more fun with math. Um, I think like in college, you know, you're, you're still like stressed out with grades and it's like, no, no, no, no, no, like, look, just go have fun with material that you're learning. Um, the GPA doesn't matter so much. You're going to end up working in a hair salon anyway, so like go, go, have fun with what you're learning, but learn something useful. 

Speaker 2: (1:09:47)
And also I would say like go, go, take, um, some like some career, personal finance and personal development classes specifically around mental mastery. You know, we have this mental mastery section. Dsdj I really wish I had, like, I had a class like that. Um, in college I would have been, I think far more successful for the rest of college. Um, you know, and I would have enjoyed a lot more. 
Speaker 1
What's the best advice you've ever received? 
Speaker 2
Um, best advice I've ever received. I think it was still that like take, take pride and humility in your work. I mean that just transcends like everything, you know, 
Speaker 1
Really, really great advice. Um, that really resonated with me as well. So how can people connect with you?
Speaker 2
 Yeah, so, um, you know, as I mentioned before, like right now I think just during, you know, during the time of COVID , um, I am taking a little bit of a, you know, stepping back a little bit from a heavy social media usage just so I can like focus on like personal development and self care. 

Speaker 2: (1:10:47)
Oh yeah. 20 year me like really engage in the self care seriously. Um, totally. It's totally underrated. Like I should've done that. But, um, I would say like right now, um, people should still feel free to, uh, you know, uh, hit me up on LinkedIn. I, I would actually say that's the best place to do so. Um, or on medium. And, you know, I love hearing from people, uh, love giving, a helping hand out, um, you know, and in engaging with like aspiring data scientists, 
Speakrr 
Mikiko, thank you so, so much for taking time out of your schedule to chat with me today. I really appreciate you being here.
Speaker 2
 Oh, thank you so much. No, it was great. This was fantastic. 

Speaker 3: (1:11:49)
[inaudible].