Speaker 2: (00:00) So I applied for 220 jobs at 20 phone screens, nine technical screens for take homes, three on-sites and one offer. So this took about three and a half months and it was a very long and stressful process, but honestly, I learned so much from it and if you're applying for a job, just keep pushing for it. That's my main advice. Just keep your head at a good mental space and just keep applying because you never know. All you need is one company to kind of believe in you to help them. Speaker 3: (00:30) [inaudible] Speaker 1: (00:43) 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 of data 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 dream job. If you're wondering what it takes to break into the field of data science, checkout dsdj.co/artists 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: (01:35) 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 you posted on the biweekly open office hours that I'll be hosting for our community. Check that out@artofdatascienceloftdotslack.com community is super important and I'm hoping you guys will join the community where we can 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 rate and review the show Speaker 3: (02:19) [inaudible] Speaker 1: (02:33) what's up artists? Welcome to a very special rising stars edition of the artists of data science podcast. During this special episode, we'll take an opportunity to speak with one of the rising stars of our industry to get an insight into how he broke into the field, the hurdles he had to overcome in a job search and how he answered commonly Speaker 1: (02:49) asked behavioral interview questions. It's oftentimes the soft skill or behavioral questions that are the most challenging ones to answer. That's why the focus here is not on technical questions. Those technical questions, they typically have black and white answers with the healthy shade of gray and you can find a wide variety of sources online for how to answer those. Our guest today is an experienced data professional with software engineering skills, sharp business acumen, and a curious economic mindset is first introduction to machine learning was during his senior year at New York university and ever since then he's been hooked by the endless value machine learning can provide to businesses and people around the world. He's inspired by data's ability to impact several aspects of our lives and has used that as a driving force to create projects with one main purpose, leveraging machine learning to solve real world problems. Speaker 1: (03:35) He's a graduate of the prestigious New York university in New York city where he's earned a bachelor's degree in both economics and computer science. His work experience includes two year run at Hudson medical group where he contributed value as a technology analyst for a growing multispecialty practice writing software and providing analysis to leverage database solutions. Currently he's working as a data analyst at cadence in New York city where he is helping the analytics team build applications and programs to automate quality assurance and aggregation of data. So please help me welcoming our guest today. One of the brightest students I've had the honor of mentoring Alex Lim. Alex, man, thank you so much for taking time out of your schedule. Appreciate you being here. Speaker 2: (04:16) Yeah, no, thanks for having me. Uh, honestly, that was a super great intro. So yeah, let's just get into it. Speaker 1: (04:23) My pleasure, man. Hey, so before we, before we jump into like the mock interview portion of this, why don't you tell me a little bit about how you got interested in data science and machine learning? Speaker 2: (04:32) Of course. So in your intro you talked a little bit about how I took a machine learning class in college and I would say that was my very first exposure to machine learning and data science. And it was funny because when I went into this class, I literally knew nothing about machine learning. I just heard of it as like a buzz word and tech companies, AI and machine learning to leverage all these fancy new technologies. So I was super pumped to go into that class. But to my surprise, when I first came into that class, I literally realized that machine learning is just a bunch of computers crunching numbers. Although that sounds a little bit kind of like underwhelming. What really resonated with me was the impact that can have on everyday lives. So companies like Facebook, Google, they're using machine learning to create self driving cars, voice assistants, anything you can think of. And it was just this impact that really kind of got me into the field and really want me to like progress forward into the future. Speaker 1: (05:22) So yeah, man must've been a life changing course man. Like, to get exposed hard. Speaker 1: Yeah. That's awesome man. So you've got some pretty awesome projects, um, on your GitHub. So can you tell me more, more about how you, how do you go about building a project? You know, how do you come up with an idea for a project? Speaker 2: (05:39) Yeah. So just a little bit of background. I was pursuing this project as I was applying for jobs and I figured I didn't have a lot of experience under my belt. So the best way to go about this was to actually do a data science project, grabs some data set online. And what I literally did was I went on Kaggle and then Google or just like look through a bunch of datasets, she would interest me. I ended up choosing a dataset about red and white wines. One because I'm a big foodie and I realize that if food, but also I enjoy a nice nice bottle of wine. I confidently company that. So it's just kind of like a natural choice for me. Yeah. So I just wanted to pick a data set that I was interested in and something that would keep me motivated to like push forward with it. Speaker 1: (06:18) I love wine as well. What's, what's, what's your, what's your go to bottle for drinks? Speaker 2 I'd probably get like a bar or peanut ratio or a cab. Speaker 1 So me being from Northern California, like I'm a definitely die hard Zinfandel, authentic Trixie, those old vine Zinfandels fricking love that stuff. When you're coming up with a project, like there's a lot you have to kind of go through and plan, right? So when you're starting out with their project, how are you developing a plan of attack? Speaker 2: (06:46) I guess what was kind of interesting about my project was that I, um, in the kick, uh, capital repository, there was two datasets. There was a red wine dataset and a white wine dataset. And since this was my first project, I kind of want to like maximize my learning. So I decided to approach them both in different ways. My red wine dataset, I used more for like a classification problem and I tried to solve the issue of, well, when I go into a grocery store and I want to find a bottle of Zinfandel, how can I tell that that's like a good quality of wine besides you telling me but I trust you. And uh, so wanted to solve that problem for the red wine set. And for the white wine, I approached him more of a regressional analysis. And from a producer standpoint of, well, exactly how can, if I know that certain characteristics of wine affect its quality, how can I maximize my workflow and processes to achieve that result? I did a lot of background research about how to go out a data science project. Just literally go on and GitHub and catego look at learning like all the steps and just went for it head on. Speaker 1: (07:48) Just talking a little bit more specifically about some of the challenges you had to overcome during the project creation process. Talk about some of the struggles you have to face kind of sourcing data, organizing your thought, the project structure, you know, how did you overcome these challenges? Speaker 2: (08:01) I think one of the biggest challenges for me was more of just the upfront technical research and implementation that you had to put into the project. So I remember for my red wine project I had a dataset that was highly unbalanced and when I was taking a machine learning class, you always have these clean data sets. You just like fit a model and you get a result. But just realizing how kind of messy data can be was challenging for me. And I really, I remember literally searching up on Google, like sampling methods, metrics to use for models. And it just kind of propelled me to have this second layer of understanding rather than just like the kind of theoretical aspect of it. So actually coming up with that second layer of understanding and applying it was pretty challenging for me. But you just kind of take it one step at a time and yeah that's what I did. Speaker 1: (08:47) So I want to get into what the job search process was like for you. Did you kind of just send out a resume and hope that somebody called you back? Speaker 2: (08:56) Yeah, so, um, well that's what I did before. Um, but as I, um, I took a class that actually taught me exactly what the do's and don'ts are for, um, submitting resumes. And I feel like the, like a very first step is to really Polish your resume, make sure that it's relevant to one, the position that you're applying for and to an industry that you might want to go into. So after you do that, make sure you have a bunch of people read over your resume, check for spelling issue errors and all that because all it takes is a couple of seconds for someone to throw your resume in the trash. So I really stress that that step, how exactly I went about it was I just every day wake up early in the morning before my, uh, my old job and I would just submit five applications on LinkedIn and then follow up by messaging people that I thought were related to the hiring process and just say, Hey, I just applied for your job and here are my qualifications. Hopefully I can hear back from you sometime and just rinse and repeat that process. Speaker 1: (09:51) Awesome. Now you mentioned that you targeted people specifically that you thought might be related to the hiring process. What did that look like? Did that, did you just, you know, did you ask people who were like random employees or was there, was there kind of a formula that you followed? Speaker 2: (10:07) If you can see in the job description what department is hiring or you can kind of get like little clues. Um, if not, I would literally just go through the LinkedIn list of all the employees, see what irrelevant, relevant positions to a data scientist are. So I usually looked for analytics manager, business intelligence, data analysts, messaging people that you think would be related to the process. But I would highly recommend to that. I'm definitely trying to message more of the people that have more impact would probably be the most beneficial. Speaker 1: (10:36) Yeah, I definitely want to go after people who have kind of more quote unquote, Are you an aspiring data scientist struggling to break into the field or then checkout Dsdj.co/artists to reserve your spot for a free informational webinar on how you can break into the field that's going to be filled with amazing tips that are specifically designed to help you land your first job. Check it out. dsdj.co/artists Speaker 3: (11:07) [inaudible] Speaker 1: (11:10) Talking about kind of the process you went to before land in your current role, like how many, how many interviews did you go over? So you applied, you said five jobs a day for a period of time. What was like the return rate for interview requests? Like how many interviews agenda going on? Speaker 2: (11:24) Yeah, I think it's pretty funny that you ask this question because right after I received my F my offer, I actually tallied up all of my stats of how many things I applied for, how many texts means I have it, et cetera. So let me just pull this up really quick and I'll share it with you. So I applied for 220 jobs, had 20 phone screens, nine technical screens for take homes, three on-sites and one offer. So this took about three and a half months and it was a very long and stressful process. But honestly I learned so much from it. And if you're applying for a job, just keep pushing for it. That's my main advice. Just keep your head at a good mental space and just keep applying because you never know. All you need is one company to kind of, Speaker 1: (12:06) yeah, definitely. Yeah cause interviewing really is like a learned skill in a sense. Like the more interviews go on, the better you will inevitably end up being at interviewing. So I know there's a lot of students out there who are just kinda like scared to go on interviews or scared to apply for jobs. But like you get better by going on more and more interviews and like Alex is saying here man, like it takes, it takes time. It takes a lot of time and a lot of effort, but it does pay off at the end. Do you have any advice for the rising stars out there? You know, who are where you want to swear? Speaker 2: (12:38) I would just say my main piece of advice is, it's kind of similar to what I just said, but really just stay consistent. Set a goal for yourself, stay consistent and say you wanna apply for five jobs a day. Do that every single day and just make sure that your mental, like your Headspace is in a spot where it won't kind of like hinder you. So something I personally did was do a lot of professional development slash self-help reading. I think that really helped kind of solidify where I want to go. Um, what kind of values I want to bring to a company as well as just who I like figure out who I am. So yeah, I think that was really impactful for me. Speaker 1: (13:13) The enemy, you know, be, I'm all about that professional development and quote unquote self-help type of stuff. Do you have any recommendations or any books that were just really pivotal, pivotal, pivotal in your, in your shift in your Headspace? Speaker 2:(13:25) I would say probably, well one of the first ones that I started off with was book called mindset by Carol Dweck. It talked about the fixed mindset versus growth mindset. I mean, I know a lot of these self help books are very kind of, some of them might be a little bit dry and like very repetitive, but as long as you just take that message from the book and apply it, apply it to like your everyday life, you're going to see kind of results. So that's definitely one that kind of really changed my perspective on things. Speaker 1: (13:50) Yeah, man. Like I remember when I first was introduced that concept of fixed mindset versus growth mindset, it really changed the way I look at myself and really made it so that I was able to just change my beliefs about what, about what learning is and how you can actually learn and grow. Um, so yeah, that's, that's a great recommendation and anybody listening definitely recommend that book. If you don't mind, I want to dive into a few common interview questions and how you answered them. Starting with kind of the, the first question that's typically asked in an interview, tell me about yourself. Speaker 2: (14:23) I grew up in California, in the Silicon Valley and not surprisingly, I'm a very stereotypical Californian slash Silicon Valley person. I loved computers growing up. I've always playing video games and I just tried to figure out how things were. So this kind of made it like a natural choice for me. When I go to college. I wanted to study how computers work, how it works on the backend. That's what really interests me. In addition to that, I actually chose to study economics because knowing how a machine works is great, but seeing actually how it everyday lives and businesses, that's what kind of got me full circle and interested in stuff. And not only that, I feel like the pivotal moment was when I was introduced or when I choked that machine learning course. Um, it was just so crazy to see all the impact that you can have using models to and these big tech companies doing stuff with it to really impact our everyday lives. Speaker 2: (15:13) I decided from that point on I really want to sharpen my skills and get into this space. One position that I'm currently in, I'm a data analyst for Caden and Kayden is a ad tech company that does a lot of advertising for its clients. So they are helping build out systems to automate data I irrigation and quality assurance to really help ingest that data into a visualization platform. There'll be used for its clients. Yeah, I feel like the skills that I acquired from my interest in computing to um, seeing the impact of machine learning as well as the professional skills that I acquired through this job would really help me fit in this company. Soeaker 1 Can you describe a time when you had to deal with competing priorities or competing deadlines? Speaker 2: (15:59) There was this one time I was actually in work and I had three separate tasks that needed to do one was to help a coworker and how am I helping them automate their workflow. So that was one thing. The second was just kind of doing my own day to day development work. So obviously that was very important because that was kind of showing the progress I was making towards a certain project. And the third task I had was actually giving out or helping deliver a client deliverable. So the process that I went through my head was kind of like, well, if I were to rank these in a scale of how much this impact, each tasks would be, it make it pretty clear which one I would tackle first and second, et cetera. So obviously helping a coworker is great, but it's probably not as impactful for the business and me doing my own work day to day, like that's a given, I need to do that. Speaker 2: (16:42) But really focusing on by client deliverable was key because clients are like the backbone of the business and if there's no clients and there's no business. So I decided to take it on the order client deliverable, doing my own work. And then if I had time help my coworker. Speaker 1 What would you say is the most difficult type of person to deal with and how do you deal with that type of person? Speaker 2 Probably people that are more on the pessimistic slash they're not as open minded. Um, I've found them very particularly, uh, challenging to deal with. There's this one instance when I was talking with a coworker and I noticed that the workflow was, it could be improved in my eyes. So I went up to them and kind of suggested some things that they can do to improve it, but they were very defensive in terms of like, well, I'm very used to this way and I'm not sure if this would work. So I completely understand that from their issue, especially when you just coming in. But what really helped was just sending up a meeting, going through the pros and cons of kind of the current workflow, what I'm suggesting, and try to find a middle ground that's like, I think that's really key. Just breaking it down into steps and figuring out a solution together. Speaker 1: (17:50) Can you walk me through your discovery process when you're starting a new project? Speaker 2: (17:52) Yeah, so, um, I would say what I would do if I were tackling a new project was, well first just to kind of do your little, your metal research. So if you know nothing about the data, just like educate yourself a little bit about it. And once you have kind of like a decent understanding of it, go to whoever's assigning you that project, like a business stakeholder manager, whatever, and figuring out what exactly they're trying to achieve through this process. And once you kind of do this preliminary research, then you can go into the data and see how this relates back to each one of um, like how this will, if doing this in your project will impact the business, et cetera. So just constantly having that in the back of your head will kind of eliminate all the waste that you do, things that you might think is important because you really want to focus on delivering as much impact as possible. Speaker 2: (18:40) One thing with me is like I really like to take it a day at a time and just stay consistent with my goals, whether that be professional fitness, personal development, et cetera. But I really see myself kind of working in the forefront of this industry really. I really want to leverage. Going back to kind of why I got into data science, obviously I'm focusing on my personal development now, but I really want to be in the forefront, develop these new impactful things that'll actually change people's lives. That's what I think is really important to me. So just kind of being in that situation and helping out with that. Speaker 1: (19:10) Thank you very much Alex. So I love how well thought out all of your responses are. Do you have kind of a formula you follow when it comes to coming up with answers for these questions? Do you practice them? Like what's your process for making sure that you're always having answers that are well articulated? Speaker 2: (19:27) Yeah, so I mean this is still kind of like an editor of process for me right now, trying to figure out what's the best way to answer. And I think the best way is just record yourself, talk and listen to what you said and just do rinse and repeat. Because although talking to someone else is great, you really want to know what you sound like through your own mic or your headphones or whatever. And just kind of going off that once you kind of get comfortable with that, maybe you can practice with someone else and they can give you like additional feedback. But I would just say it's just a very iterative process of just practicing caring what you said and keep keeping on that cycle. Speaker 1: (20:06) What's 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 looking Forward to seeing you Speaker 1: (20:32) I think there's, um, there's some kind of hint or underpinning of the, the star framework, the situation task action result that's baked into your responses. I don't know if, if that, if you're doing that consciously or not, but I can definitely pick up on it just because of the way your responses are so well thought out. Speaker 2: (20:48) Yeah, that's, no, that's definitely true. Uh, I always learned that you learn in your second grade English class specific examples are always the best way to prove a point. So always have some sort of story or, so to back up your whatever question you're answering, Speaker 1: (21:04) So what's your process for coming up with questions to ask during an interview? Speaker 2: (21:09) Yeah, I think this is pretty interesting. I would say that the, the way I tackled that was I would, if you have a chance to kind of research who's asking these questions, you can kind of gauge what kind of questions they would enjoy answering or they'd be most knowledgeable about. So let's say that I'm having an onsite interview with an engineer and an HR person. So obviously with the engineer I can ask for technical questions and be like, what's your current system using? How can I help improve this? How does this affect my role? Et cetera. So I'm sure they're very passionate about what they're working with. So you're going to get some good responses. And secondly, like if you're talking with like an HR person, you can ask him about like company culture. What do people do here for fun? All those kinds of questions. So just really curating your questions to the specific type of person. I think we'll probably get a lot of success. Speaker 1: (21:56) Got one last question before we jump into the lightning round. What's the one thing you want people to learn from your story? Speaker 2: (22:01) It's kinda like a motto I had in my life. It's just don't say you're going to do it, just do it because the moment you start doing it, you're going to start, even though you're failing and at first and not you're not good at it or whatever, just keep doing it. You're going to get better and better each time. And I think this kind of relates to the field of data science, machine learning. It's such a new field that there's constant competition. You need to find a way to stand out from the crowd. And I think the best way to do this is just you gotta keep consistent what you're doing and just keep at it. Speaker 1: (22:31) Got a few lightning round questions for you. First one here, Python or R? Speaker 2 Python. Most are. I mean I'm familiar with R, but Python definitely. Speaker 1 Yeah. Awesome. So what's a book every data scientist should read? Speaker 2: (22:44) Actually, well I haven't finished this book, but I'm reading this book called effective Python by Brett slackin. I think this is crucial because I think code quality is essential to every data scientist slash engineer. So reading a book that'll really help you write efficient code is, Speaker 1: (23:00) okay. So I'll put that in the show notes as well. He said it's called effective Python by Brett slackin. What's your favorite question to ask in a job interview? Speaker 2 Kind of like a default. The question I have that I like to kind of throw into the person interviewing is given all of the credentials I have, what's one, what's one thing you think I might struggle with on this job? Because it'll kind of bring light into what they might think your weaknesses are and how you can help prove to them that you're the right candidate for this job. Speaker 1: Yeah. Awesome. Ready? Cause you're saying I, you might think I have some weakness. Let me go ahead and address that right here. Right now. So we can clear that up. Awesome. I like that. Would you recommend that up and coming data scientists focus on certifications, self directed learning? Speaker 2: (23:46) I would say definitely. Um, well whatever will get your foot in the door if you think that uh, certifications will really help motivate you to actually do what you set off. You're going to do to that. But I think self directed learning in the long run is a better kind of way to go about it because this not only applies just to your professional life, to your personal life as well, because once you're out of school, there's no one telling you what to do. So you've got to make sure that you do it. You make it happen. Speaker 1: Awesome man. How could the people connect with you? Speaker 2: LinkedIn, if you're a part of the DSdj the slack is fine. Email, anything. Honestly, if you need anything from me, just let me know. Speaker 1 Awesome man. Look, Alex, I really appreciate your time. Appreciate you being here with me today even though we kind of separate some technical difficulties first started. But again, man, appreciate you sharing your insight with everyone. That's listening in. I know there's so much that people take away from this conversation, so thank Speaker 2 Yeah for sure. Always a pleasure and thanks for having me. Speaker 2: (24:48) [inaudible].