Sundas Khalid Mixed.mp3

[00:00:00] I think not just Data science, I think it's any technology that you can think of, you can have like good and bad uses, but it's at the end of the day, you can build a technology, but it's how humans end up using it. And if there are humans who will tend to be biased, they're obviously biased and they're good people and they're about people. So depending on whose hands the technology is in, its application is going to be different.

[00:00:40] What's up, everybody? Welcome to the artists of Data Science podcast, the only self development podcast for Data scientists. You're going to learn from and be inspired by the people, ideas and conversations that'll encourage creativity and innovation in yourself so that you can do the same for others. I also host open office hours. You can register to attend by going to bitterly dot com forward, slash a d. S o h. I look forward to seeing you all there. Let's ride this beat out into another awesome episode. And don't forget to subscribe to the show and leave a five star review.

[00:01:35] Our guest today is a data scientist and advocate of women's education and workforce diversity.

[00:01:42] She's the first woman in her family to graduate university, earning her bachelor's from the University of Washington, where she graduated as the class valedictorian. She's currently a senior analytical lead at Google. And before that, she was a data scientist at Amazon where she was awarded for her work driving large scale experimentations and data science initiatives outside of her day job. She runs a business coaching students and industry professionals to level up and excel in their careers. She's also an accomplished speaker who regularly participates in motivational and technology talks. So please help me in welcoming our guests today, a woman who is driving the direction of tech towards women through her accomplishments and achievements. The friendly face of change from this college on this. Thank you so much for taking time out of your schedule to be here today. I really appreciate you swinging by for the second time.

[00:02:41] Terfry, thank you so much for such a kind introduction. It's an honor to be here and I'm happy to chat with you today.

[00:02:47] Definitely. I'm excited to dig into your background and kind of understand how you got to where you are today and where you see the field heading. But before we do that, talk to us a bit about how you first heard of Data science and what drew you to this field.

[00:03:02] Yeah, so for me, the path was not as straightforward for me. I had kind of figured it out and I accidentally discovered I was actually going to business school to finish my business degree when I took one class and information systems that taught me about databases. And that was my brief introduction to databases and SQL. And then I ended up doing an internship at Amazon, which was also and I loved, by the way, when I took that class, I loved school.

[00:03:32] But at that time I had no idea. There is a world of Data science out there. And that is like seven years ago. And I think at that point Data science, but still not very well known. It was pretty new when I did my internship, but I was on when I was interning as a financial analyst intern. During that period, about eight months of that internship, I kind of started working with people who were working with Data quite a bit. And that's how I got an exposure to the Data science world. And since I was exposed to mainly the databases and SQL, so I thought like my next step was to pursue a Data eggrolls that is focused on the engineering aspect of it. So I ended up finishing my internship. I took a few classes outside of business school, like to build up my Data engineering knowledge. And then I interviewed at Amazon again for the technical roles as a Data engineer. And I passed that screen and then I ended up joining that role full time as a Data engineer. And I pretty much whatever I started doing when I was working as a Data engineer, I whatever I did, I learned on the job.

[00:04:40] I did not apply any of the knowledge that I learned in school because I went to business school. So but I got lucky in the sense because my teammates were very supportive, my senior team, it kind of caught me quite a bit. And that's how I kind of like managed to do really well as a Data for about two years. And that after two years, I realized that I was building the Data pipelines. I was generating a lot of Data, but then I was giving it to somebody else to take care of and do the analysis I presented. And I it also bothered me a bit that by the end of the presentation, my name was kind of somewhat lost in the pipeline. So and then I ended up doing one data analysis project that kind of made me more interested in the field. And following that, I ended up transitioning to a Data science role in a different team that I was exposed to a lot more data scientist in my teams and a research scientist and a lot of software development. So I kind of got exposed to a lot of people around me who were already in Data science.

[00:05:40] So I got to learn from their work. And that's when I took online courses. I had books on my own and my manager gave me a bunch of projects in Data science. I did those and what I would learn and then I would apply that. My manager ended up transitioning my whole family from Data engineer to Data science. So that was my Zig-Zag journey from the school to being a data scientist. It was definitely not clear. Even when I transitioned to data science or family, there was no job family called data scientist at Amazon at that point. It's actually the only launch. Right before I left, I was on it was getting launched. I think you probably would know this as well. Like how new the data science field is. Things are not very, very clear and the definitions of self are not very clear. Some people are data analysts. It's consider in those companies it's considered data science. Some companies considered applied scientists to be a data scientist, so it's like a mix of things. But I think for me personally, what was helpful was having people around me who are already doing those things could learn from them and get inspired from them.

[00:06:47] Thank you very much for sharing that. I really appreciate the part about the squiggly career path, because I think a lot of times when people look at a resume or look at a LinkedIn profile and they see somebody like you who's worked at Amazon and at Google, they just assume that it was a just a linear progression or just one after the next. But it sounds like when you first started out, it wasn't like you had planned to end up a data scientist to kind of just weave into it. What were some key struggles that you had faced during this squiggly path to where you are now? That looking back, if you were to share advice with somebody who might be going through those same things, like what would you say to them?

[00:07:28] Yeah, so I think there were many things I would say. I did it the hard way. I wish I had done it that way. If I had known what I wanted to do, I would have definitely pursued a degree program like a full degree program instead of like zigzagging my way through it because like I did struggle, even though, like I learned on my own, I did struggle quite a bit. For example, their core terms that are like a part of Data science.

[00:07:55] For me, it took forever to kind of figure those out. And I felt really stupid and dumb asking questions. For example, that is something called feature editing. When I was transitioning from Data didn't introduce AIs and I was learning and this term was like getting thrown around all the time. And I was just like, so confused, what does it actually mean? And I was just like feeling dumb to ask people because I'm supposed to be like this because I'm trying to transition and stuff. But what is doing basically is if you're just selecting what features, what qualities you want to include in your model and stuff like that. So like they were like small, simple things like that, because I didn't have the core foundation. I kind of struggled and I had to get out of my comfort zone a lot of the times, and I had to put extra time learning things and figuring it out and simplifying it for me so I can make that transition from kind of like business on techie side to a tech site and also like more science side of things. And I think on top of that, as I mentioned before, that science is not very clear family. So I struggled quite a bit, understanding what my path looks like, what I want to get out of this role that I am in, and also not just beyond that. When I was looking for jobs outside of Amazon, I was struggling quite a bit because Data science family at Amazon did not match the Data sized family at other companies.

[00:09:16] So I think for me that was kind of like a some of the confusion points as well as the struggle points. But if I were to, like, do it all over again, I would do it now because it's really hot and there are a lot more degree programs that are out there that are more well built for me to have more knowledge and get like a more core understanding of the concepts and things that are being taught in colleges and degree programs. And obviously it's not in my hands, but if it was up to me, I would kind of like standardize the Data science fambly itself, because I think for me it's a it's a big frustration to like jump companies when the mind roll does not map very well. I have to do a lot of research on my own to kind of figure it out. And the last one, which is also outside of my pay scale, is what I would change is the career path. Currently Data scientist, I have experienced that I eses. There's not many Data science managers, so I struggle with that quite a bit. If I were to climb up the ladder, how am I going to do that in a Data science career when there are not many management opportunities?

[00:10:25] Thank you very much for sharing that. And you've had an opportunity to look at some of the most cutting edge companies, Amazon and Google. I'm wondering where do you kind of see the field going in like two to five years?

[00:10:39] Yeah, I feel like Data is the future for sure. I was just talking to somebody recently where we were discussing how much Data is out there and somewhere around billions of gigabytes of data and only one percent of the data is actually analyzed today. So all the things that where we're taking data and we're turning it into information, only one percent of the data is analyzed. And if that's the case, then we know that there is still a huge amount of data set that is yet to be analyzed. And that alone in itself is kind of reassuring that the data is the future and there's going to be endless opportunities in the data space, specifically knowing that going one step further, I think A.I. and machine learning are going to continue to evolve. Yeah, there are some areas where we can improve and I and machine learning as well. But I think we A.I. is going to kind of not take over the world, but it's going to have a huge part in not just daily lives, but also like in a lot of the things that we currently, it's not part of it.

[00:11:44] So it's interesting when I hear, like, people asking you that, oh, is Data jobs going to be affected because of covid or what not? As part of data dream job, we've got a bunch of Manti's like two thousand plus. And that's the most common question they ask is, is data science still going to be relevant in the future? And it's like we you think about it now. If any field is going to be accelerated because of this COGAT situation, it would be data science because we are generating more and more and more data now than we used to.

[00:12:15] So, yeah, absolutely. So I was I was forgetting that number is actually 40 trillion. Gigabyte of data is just generated, going to be expected to be generated by 2020. And 90 percent of all the data that exists is actually created in the last few years. So when it's somebody laughing, you feel free to throw these numbers because just looking at those numbers alone, you can tell that Data is the future. And there's a lot more opportunities there, too, that we can like things that we can do with the data that we're currently not doing is mind boggling number.

[00:12:49] So going into the future with all this data being collected from pretty much almost every aspect of our lives. Right. All of us have devices and and things like that. What do you think would be the biggest, scariest application of Data science machine learning in the near future?

[00:13:09] I think not just Data science. I think any technology that you can think of, you can have like good and bad uses. But it's at the end of the day, you can build the technology, but it's how humans end up using it. And if there are humans who are humans who tend to be biased, they're obviously biased and they're they're good people and they're bad people. So depending on whose hands the technology is in, its application is going to be different. So a few examples I could give here is that let's say like somebody is using unauthorized data and an example would be like and if somebody takes the election data and temper's it, and because of that, a decision has taken, a country's future is dependent on it. So so I don't think it's a specific animal. It's actually about who's building it, how inclusive it is, how fair it is. And then the second part is who is actually ending up using it? And that's where it comes down to. If it gets in the wrong hands, then obviously it's use is going to be wrong.

[00:14:16] So as we move to this future where things could go bad, depending on who's using the Data, you mentioned that it's up to the people who are upstream, people like us who are developing these tools. What do you think should be some of our biggest concerns and the things that should be at the front of our mind as we're doing our work day to day?

[00:14:39] I think at the as a Data, as Data people, we should be more aware of not just the technology that we're building, but who we're building it for. For example, if we're building a new machine learning model, very simple example, as if you are building a shoe model like shoe recommender model. When you're thinking of shoes, when you think of a shoe, when I ask you to imagine a shoe, when or when am I going to shoe? We're going to think of two different shoes, like different type of shoes you might think of. I'm going to think of flip flops because that's what I'm wearing. There's this. You might think of something else. So I think it's all about we all have our own biases and some are explicit, some are implicit. And the implicit biases are basically unconscious biases. And all humans have it and not purposefully. But those biases do end up translating into the technology that we end up building. So I think just getting more educated and more aware of this is important, but also kind of like putting the ethical and social responsibility at the forefront when we're trying to build something new for a group of people that we want to we want them to use.

[00:15:56] And also, I think one of the things that we can also do, which is going a little off topic, is let's say I was actually giving an example in an earlier talk where a study was done, where they found that self-driving cars tend to are highly likely to run into people with dark skin tone versus people who are lighter skin tone. And then the study was actually going further and saying, like, maybe it is happening because the data that they have, the underlying data is majority of the people in the data is light skin tone. And there's not a lot of samples for dark skin. But they're also saying that even if there are a few samples, whoever build the model, they did not put higher weight on the few samples that they had on the darker skin tone. So like things like that, when we're building things, when we're building algorithms, we take a lot of decisions.

[00:16:50] And those decisions a lot of times are based on how we think and the way we can kind of be more inclusive and more diverse is like having more inclusive and diverse teams, um, but also like being aware that things like these can happen. So we should, like, build more systematic way to figure out if think of something like this to happen and to create feedback loops. So we're not just building something and launching it, but also going back and seeing if that model or whatever you build is performing the way it's expected to perform.

[00:17:22] Thank you very much for that. I really appreciate that response. So what do you think is going to separate the good data scientists from the great Data scientists?

[00:17:33] I think that answer is very simple. Basically, a difference between a good and a great Data scientist is communication. We know that Data science can be a complicated field. The purpose of doing all the data science work is so we can impact the bottom line of the business. And in order to do that, a lot of the times we have to pitch our own work to non tech individuals.

[00:17:56] And if we're not able to do that successfully, if we're not able to advocate for our work, and if we're not able to translate that very complicated science and technical aspects into business, our pitch might not take off. Our model might not get production AIs. So I think that's what differentiates, in my opinion, a good data scientist from a great data scientist, that they are not only able to do the work that they are doing, whatever that is, but they're also going one step further and making sure they're connecting the dots with the business. And they're also able to communicate with the audience and the business audience.

[00:18:31] Absolutely agree with that. So I'd like to get into some of the writings that you've done. You've done some really awesome pieces on medium and also some stuff from some of the talks that you've done. But it Data science. There's a lot of resources out there to learn a particular concept, to get a tutorial on something. And sometimes you just. Get in a cycle of AIs went to a tutorial after tutorial. And you talk about this nicely in one of your articles, The Reverse, The Curse, the Tutorial Trap. So let's get into that. First of all, talk to us about what the tutorial trap is.

[00:19:10] Yeah. So basically the territorial trap is and I did not coin this term, somebody on Twitter there and I happened to come across that tweet. So a tutorial trap is basically as humans. So I can remember the study. But there was a study done that was saying that our brain functions very similar to how we get hungry and we want to eat food. And similar to that, our brain functions that we crave for learning and we want to keep on learning. And what happens is that when we take our ticket, we want to learn something new. So we end up taking either, of course, either YouTube video or whatever that is. And we will take that. Let's say if it's a 20 minute video, we'll watch it. And then what happens is that a lot of the stuff that is being taught is very high level and intro level and very basic does not mimic the real world. So when we take that course, it seems like at the end of the 20 minutes or when we watch that video at the end of the 20 minutes, it seems like we got it, but we actually haven't. And our tendency as humans is to move on to the next thing. So you take that 20 minute video and then you take another 20 minutes. And two hours later, you have watched four videos and in your head you're thinking that you know it better when it's time to apply it.

[00:20:28] You kind of struggle and you're like, I put that video, how come I don't know how to do this? So that's where the tutorial trap comes in, where you feel like that, you know. So you keep taking tutorials off the territorial and in but in reality, when you actually end up applying this in the real world, you're going to run into a lot more problems that you didn't learn how to solve. So one way to solve for this tutorial trap is take a pause. Let's say if you took that 20 minute video, take a pause, reflect what you'll learn and apply it. Once you apply it, you will come across those real world issues, those complications that you didn't see in the in the course. And that will actually solidify your experience once you have solidified that knowledge only then move on to the next part. So, yeah, this is like one of my favorite topics, because a lot of the time people say, like, we took this course, we took this course we know this, but when it comes time to apply it, they actually don't know this because they only took the course. That did not apply it.

[00:21:31] Yeah, because just sitting there watching a video on YouTube, it's a very passive act. You could sit and watch a video, but have some of that stuff just kind of come in one year out the other. I think a great way to overcome that tutorial trap is as you watch the video, just like do a brain dump, just write down everything that you learned in the video and like you mentioned, immediately start applying it so that you can bring that concept to life. So there's another really inspiring talk that I saw you do on embracing failure and building a growth mindset. The growth mindset is something I feel is super important. And I wanted to get into some of that stuff that you talked about. First of all, how did you hear about the growth mindset? How did you come across this concept?

[00:22:17] So I was not exposed to this concept then the term. It's the terminology itself. Early on, I actually came across this terminology when I was preparing for one of my talks. And when I saw it, I realized that I've already been doing it, but I didn't know. I had no idea what it's actually called. So basically, there are two kinds of mindsets. One is fixed mindset and one is growth mindset. A person with a fixed mindset thinks that whatever they're born with is the end of it. So if they're born with a skill, they cannot learn a new skill and people with a growth mindset, they think that they believe that they can learn whatever they want, they can learn after putting in the effort. So I think how it comes into and thought that I was talking about is about embracing failures and how it came with the picture is because I in my life, I have failed quite a bit of time. And every time I failed, I would get up again and try a different method and try something else. And I will achieve it the second time, the third time. And that that that is all.

[00:23:27] What's a growth mindset is about. Like if one thing doesn't work, for example, English is my second language. So I when I was trying to apply for business school, I actually got rejected the first time because I my English was not great. And the business, what makes you like take essay exams and all of that? And I completely failed that. So if I had this mindset, I would have stopped then. But I said no. Even though English is not my first language, I'm going to put more effort into it and I'm going to learn it. And I ended up doing that for six months and then I gave. That test again, and then the second time I did OK, and I actually I ended up making it to the same business school where I was rejected from. So that's the whole idea about like a growth mindset versus a fixed mindset. There's one of my favorite quotes. Do not change your goal, change your method. And that's what this whole growth mindset is about, that keep learning, try different things. If it doesn't work one way, try a different way.

[00:24:26] Absolutely love it. They let you into the business school and then you got in and then graduated valedictorian give you your speaking English?

[00:24:35] Yes, that was amazing. That was like once in a lifetime. And that's like a good line now that I can tell people like, hey, look, I got rejected, but I got accepted and I ended up graduating from the same school where I was. I they chose me to give a speech to like three thousand students the graduation class. And I was standing there and giving a speech in English in front of all those people. And that was like a full circle for me because that was the same school that I was excited for my English.

[00:25:02] Yeah, I absolutely love the growth mindset. Carol Dweck spook is amazing. I think I came across a growth mindset way too late in life like I was in my mid thirties when I heard of it. And I just wish I was exposed to me at a younger age. And now that my wife and I have a five month old baby upstairs, I'm going to make sure he learns about this concept at a very young age. So for the people out there who think that this mindset stuff, this growth mindset stuff is just a bunch of fluff and it's not going to help them at all, what would you say to them?

[00:25:37] I would say that try it like don't think it's the fluff. Like, try it for yourself. Maybe it will work for you. Maybe it wouldn't work for you. But if it does work for you, that's wonderful. And that's exactly what you want. Calling it a fluff. It's fine. But if you haven't experienced it yourself and if you haven't put in the effort, then that doesn't make sense. And if it's working for a lot of people out there, then maybe there's something in it that it actually ends up working for other people.

[00:26:07] Yeah, it's just a matter of updating your belief system. Like, why would you want to have a belief that makes you that leads you to think that your intelligence is like your shoe size? Like I can't really think your shoe size bigger, but you can think your mindset into a more productive mindset. So tosspot imposter syndrome. I love this article you wrote on how to manage it. So starting from the top through like what is imposter syndrome and why is it that so many smart people fall victim to it?

[00:26:36] Imposter syndrome is basically it's it's called a syndrome, but it's actually a phenomenon where people who are accomplished, they are not accomplished like people in general, men and women, they think that they are accomplished because they got lucky and they feel like a failure many times and they feel like that. The reason that they got here is because of pure luck. The reason I feel so attached to it and because I've written so many articles and given thoughts on it is because I go through it quite a bit. As you mentioned previously, I'm the first female in my family to graduate from university, and I come from a very male dominated culture. And I'm currently working in tech in a very male dominated field. So things don't get easier for me even after breaking through those barriers. So I suffer from imposter syndrome quite a bit because I don't see a lot of women who look like me. I don't see a lot of women who have the same cultural struggles that I did. So a lot of the times I felt like maybe I got here because I got lucky. And I remember like there was one meeting when I was at Amazon and I was presenting to this big project that I've worked on and I was presenting to a roomful of seniors.

[00:27:50] And I was so nervous that I thought, like, that's never going to present. They're going to figure it out that I'm basically a fraud and I'm not as smart or as good as I perceive to be. So it's basically a feeling that it's all in your head. In a study done in UK mentioned that said that eighty five percent of the men and women actually experience it. But the reason it's not so well known is because people don't talk about it like you would never say, like your senior manager come to. You're like, hey, I felt like a failure today or I am going to present this talk. I present this presentation, but I don't think I'm going to I don't feel confident enough or good enough to talk about it. So it's not normalized as much. We are all of this pool to fake it. Yeah, fine. Fake it, but also talk about it at least in your safe circle and with your friends and your close like friends and group of people that you can trust, because the chances are if you are going through word, somebody else is going through it and with it as well. And so you are not alone.

[00:28:57] And do you have any strategies? You can show this to overcome imposter syndrome.

[00:29:04] Yeah, absolutely. I have a few strategies that have worked for me. Number one, I think the biggest one was the physical one, where visually I didn't see a lot of women and a lot of women of color, especially from black, descended around me. So I ended up joining an organization of Pakistani women in computing where I am surrounded by a lot of women who are from Pakistan and they are successful in their careers and they're doing very well. So when I see them doing really well, I get confident. And if they can do it, then I can do it as well. So I think that personally, that helped me quite a bit, just like getting my confidence level up, because that made me realize, like, no, I'm not lucky because they were not lucky. They worked hard. I worked hard. And that's why I'm not lucky. The second thing that helped me is that keeping a log of all the failures as well as all the successes and also all of the hard work that I've put into it, because let's say when I'm feeling like that, I got here because I'm lucky. It's not because of that. I will remind myself of all the hard work that I did to make it here, whether those are like staying up all night to study or getting rejection after rejection, but not giving up or getting rejected from school, but not giving up and things like that to kind of like remind myself constantly to that I did not get lucky, actually worked hard to get here.

[00:30:25] And the last thing is like don't compare yourself to somebody else's success or somebody else's out earlier, because the chances are they are also going through something similar but that they're not talking about. But chances are the other person in front of you is also having an imposter syndrome. And the last thing that I actually did yesterday because I was really nervous for a talk that I was giving today, so I just stood in front of a mirror and like some of you got those like you give this talk, you give the sock. You did so well in all these talks. Why are you nervous for this one? Pep talk for me personally helps quite a bit. So I will try I will recommend trying any of these or all of these strategies combined to kind of like see what works for you best.

[00:31:11] What's up, artists? I would love to hear from you. Feel free to send me an email to the artists of Data Science at Gmail dot com. Let me know what you love about the show. Let me know what you don't love about the show and let me know what you would like to see in the future. I absolutely would love to hear from you. I've also got open office hours that I will be hosting and you can register like going to Italy dot com forward, slash a d s o h. I look forward to hearing from you all and look forward to seeing you in the office hours. Let's get back to the episode.

[00:31:57] So I'm talking about being a woman in Data Science. What can we do to help foster the inclusion of women in Data science, get their voices heard and make sure that they are feeling included?

[00:32:12] Yeah, I think I would say two things. One thing I would say to the women is, first of all, this is not a women's problem. So I don't think the burden falls on women to solve this. But one thing I would say that a lot of women feel isolated because they are the only females in their teens or on their floors or so like join a community, because community, for me personally has done quite a bit. It's made me feel less lonely. And when you feel less lonely, you feel a lot more comfortable and confident in what you have to offer. So a women, I would say, like join a community and find yourself group there. You can actually talk about difficult conversations and get advice. Not going back to my previous comment where I was saying that having more women and making work environment more inclusive, it's not a women's problem. I think everybody needs to get involved. We need to have more allies who are willing to be willing to support us, willing to mentor us, willing to sponsor us. And that's the only way we're going to move forward as a community together. Well, and I think, like, if you're a leader in your company, like evaluate what are the diversity programs that are out there?

[00:33:29] How are you supporting and retaining the women who are currently working in your organization? I feel like a lot of the work organizations do to recruit diverse talent, but the efforts stop there. A lot of times where there's not a lot of work is getting done to retain the talent.

[00:33:48] And that's why a lot of women end up leaving, especially the tech space. They end up leaving it to leaders. I would say like evaluate what are those policies? What are those flexible? Ladies, and first, that you're doing to kind of retain the talent that you already have and helping them, supporting whatever, whatever they might be going through.

[00:34:10] Yeah, absolutely agree with that. And I think it just starts with just having a willingness for men, especially. You just have a willingness to just listen to everyone's voices, listen to the women and Data science voices. I find it very disappointing that, like just looking at numbers, my podcast like the amount of listeners that the women of Data science get compared to the amount of listeners that men get, it's really not an equal distribution. And I find that to be very, very disappointing and just start listening. I started listening to the women in Data Sciences, Voices and Perspectives.

[00:34:50] Absolutely. Thank you for saying that.

[00:34:53] So what are some soft skills that candidates are missing that are really going to separate them from their competition?

[00:35:04] I think there are two things. One is I talked about previously communication. Having especially as a data scientist, having good communication is very, very important because you are going to be talking with the technical teams as well as non tech and business teams. So for you, communication is very, very important. And just adding on to it is you are not just giving data or doing things, you are actually building a story out of it. So storytelling is very, very important. So I think it also fall into the communication part of it. So a lot of the newcomers, I feel like they focus their energy on quite a bit on building the models. I think that's fine. But you are going to be involved in the whole lifecycle of the data science project. So you need to get exposure in all of those areas and not just one part of like building the model. So for me, I think one thing that really, really helped, as I understood the how the data pipelines work and I was self sufficient to some extent that I was able to pull my own data and do things on my own. So things like that can kind of help you make a better data scientist, but also give you a leg up other over other candidates as well.

[00:36:21] Thank you very much for sharing that. So you talked about storytelling and the importance of storytelling as a data scientist. Do you have any tips that you could share with the audience for how to be better storytellers?

[00:36:34] Yeah, so I think it's not let's see. I'm trying to think of an example where a God where we can kind of give an example of storytelling. So when we say storytelling, when we're not just telling you to like say like once upon a time and things like that, there's nothing like that. When we're saying tell a story with the Data, if basically we're asking, like, tell me what business problem your solving and how is going to help the end user. So start with the problem that you are solving with whatever you're building and then translate into the final business and customer outcome. And that's how you can build a story as you start with the problem you say like this is the problem of solving and with the solution that you have out, it's going to help business this way. It's going to help customers this way. For example, it's going to generate a hundred million dollars in the next year. And no, customers don't have to worry about getting logged off randomly and things like that. Whatever you are, whatever whatever your model is doing.

[00:37:39] And for Data scientists who find themselves in a room full of executives and they need to communicate their ideas. Is there a kind of a code switching or a different way of talking that you need to do?

[00:37:53] Yes, definitely. Code switching, definitely simplify it. Do not share too much detail, share enough details and also talk in their language. And that is the point where you don't want to show off because you are trying to kind of like pitch and you're trying to get the buy in. So make it very simple to understand for the non tech people and the senior executives now understand who you are talking to. If you're talking to senior leaders who are technical, like change your language to that. If you're talking to senior leaders who are non-technical in your language, do that. So just like read the room and read or you are presenting to who you are pitching to and then go from there.

[00:38:34] And earlier you're talking about how early on in your career there's some situations where you didn't understand the meaning of something like, for example, feature engineering and that you're hesitant to go talk to your team members and get their insight. What advice would you share to someone who's working on a team and they're scared of not looking like they don't know something?

[00:38:58] The first advice, I would say. If you knew the questions, because that is your time, they're giving your time to on board, so that is a perfect time to ask the questions that dumb. Promise me they're not like they're not dumb. So ask those questions in the beginning. But even if you have questions like after if you're not a new hire and if you're not in your own big time, like, feel free to ask them now rather than waiting building, you're doing your work and then delivering the final thing. And then you realize that it was a mistake and that could have been easily solved because you didn't ask the question. So save yourself all that, that and things that could go wrong by you not asking that question now vs. vs. looking at the final product and then realizing that you should have us. So ask the questions. And one thing that I like to do with bit informal is that I like to go on coffee shop. So I would have like a few people in my team who would I would have one on one. And a lot of the times I tried to like kind of bucket all my silly questions and simple questions during that coffee chat, because it's like an informal discussion. And I don't feel like I am being watched by other people when I'm asking a silly question.

[00:40:15] Thank you very much for sharing that. Really appreciate that advice. So, again, just continuing on this theme of soft skills, here's something that I'm sure you've got some great advice and some maybe great examples of what not to do talking about networking tips. So do you have any tips for the audience listening on how to network with people? Both. Let's start with online, because I think that's the most common way of networking now.

[00:40:44] So far online, I think it's it's easier now to some extent, because now you don't have to approach a stranger in person. You don't have to interrupt in a way, existing conversations to kind of like in the physical networking session. I would like there would be a group and I want to talk to one person in that group and I will just go stand there for a few minutes when the conversation is over and then I'll try to grab that attention. So now I feel like from being online, it's a little bit easier, but it's also challenging because we don't have many in group like those physical conversations where we have like group regular groups getting formed. But there are still opportunities to network online. And I would say like one of the easiest way to network is if you're counting online conferences, if you are attending webinars, take notes of the people that you are meeting in those webinars and in those conferences. That's like one of the easiest way to kind of build your network. If you reach out to somebody new who you haven't met before, have a really good elevator pitch ready and you will is the LinkedIn to send your messages, use LinkedIn for that as much I get a lot of and I feel like that's a waste of message.

[00:42:00] I would get a lot of weird messages like Hello Hisam. Those are things that don't have context. I'm not going to respond to that. You need to have a really good elevator pitch ready and you need to make sure that you have done the research on the person that you are reaching out to with your cold messaging somebody. And you know why exactly you're reaching out to that person and how they are going to help you, specifically what is expected of them that only they can help and nobody else. So make sure that you personalize it. You share what you what your elevator pitch is and what you actually want out of this conversation. But I would say, like, if you cold messaging, that is the best way to approach people online. But I think another like another people are overlooking this. I have noticed that. But like the conferences is like one of the best ways to kind of build a network. And there are a lot a lot of free opportunities out there since now that we are kind of like a global community because everything is online, the the number of people that you can meet from different regions is just endless right now.

[00:43:06] Yeah, I wrote about this recently ish on LinkedIn where I was pretty much just saying, don't be weird when you message people like I get hundreds of messages every single week and at least seventy five percent of them are just high and that's it. And it's just like, why? So you wouldn't just knock on someone's door and just stand there and say hi and just sit there blinking, looking at them like, that's so weird, don't do that.

[00:43:33] Yeah. The only high I would expect is if my director is sending me a chat message at my work chat and saying hi and I would reply to him some things I need to respond. So do not do the.

[00:43:46] I agree with you and don't just message asking about jobs. That shouldn't be the first message. You send someone like, hey, can you get me a job?

[00:43:54] One more thing I want to add here. A lot of people would just send me their resume. And I like I do not respond to all the messages. And I am giving you an example of people that I do not respond to is they will send me a resume. They would have no clue what they want to do. And they like, can you look at my resume and tell me which job is good for me? There is no other way to do your homework for you. Please do your own homework and then reach out to me and then tell me how exactly I can help you. Everybody is busy, so you should be very mindful and respectful when you're asking for somebody to give you a favor.

[00:44:27] Yeah. Make it easy for somebody to help you. Make it easy for them to get you whatever advice or insight it is that you are looking for. So you also do career coaching services and career guidance services. Do I talk to us a little bit about that?

[00:44:43] Absolutely. Yes, I do offer career coaching services and most of it is mainly the resume interviews, interview props and also like just like General Q&A career kubernetes. You can pretty much anybody can go and set up the time. It's on this call, a dot com slash career coaching. And it's basically we will set up time where you will kind of like walk me through your use case and I will guide you in terms of next steps and also share my perspective on things that you should do it differently if something is not working out. And a lot of the people that I get, I'm surprised to see how many the number of mistakes that people make that are easily solvable. If you were to like do a little bit more research and also like job strategy, like job search strategy and interview, perhaps those those I definitely understand. But if you are interested in getting a career coach, make sure you actually know what you actually want to get out of that conversation because that person can only help you. What you.

[00:45:44] Out of your career, thank you very much for sharing that last bit of advice to. Yeah, but you need to know what it is that you want. We can't help you. Like we can look at your resume and tell you what a good job for you is like. I don't even know you. We're talking about diversity in Data science earlier. And you wrote a pretty interesting piece. I think for the most part, everywhere we go in the Data space, we're kind of in the majority. I mean, we're brown skinned people, but there's not a whole lot of representation from our colleagues that are black African-American. And you wrote a great piece a while back on how we can be allies to them. I was wondering if you could speak to that piece and kind of walk us through it.

[00:46:26] Absolutely. And thank you so much for reading that piece. I really appreciate you bringing it up. So I basically wrote that piece to kind of I am an immigrant and I did not grow up in America, so I don't know, like my school when I was going to school, I did not get educated on American history like my my studies are focused on my country's history. So as an immigrant, I felt like even though I'm living in this country, I don't know a lot about the history, even though like the make you study some like US history and the citizenship test and stuff like that, I felt like there was a lot of unknown for me. So I ended up doing this research in terms of like how I can be a better how I can be better educated and be better able to specifically black people who are experiencing a lot of racial injustices. So in this case, I basically talk about like five strategies. One is like, number one, educate yourself, whether that is through reading a book, whether that is talking to people around you, whether that is watching a documentary or whatever your learning form is, educate yourself on what is happening around you and where you actually fit into it. How can you help? And that's where you come in and you identify what are the privileges that you have. And I personally feel like I am, even though I'm brown, my skin is a little bit lighter skin tone.

[00:47:48] So I that is my privilege that a lot of times maybe I avoid some sort of racism, racism, because I do not look dark enough. So that is like one of my privileges. So identify your privilege. What exactly is it that you are getting? You are basically basically what do you have that other people around you don't have the other things that you can do like things. When I wrote this piece during that time, black people were getting shot, things like those were happening. So there were a lot of ways to for you to sign the petitions. So sign those petitions if those that are already there donate to those causes, because there are a lot of good organizations who are doing the work to support those people. And it is very uncomfortable to talk about these things. So don't don't avoid the discomfort, quickly identify it and move past that, move past it and continue that conversation. And don't just stop the conversation when once that big, the news stops talking about it. It's your responsibility to keep the conversation going. And even when everybody else is forgetting about it, make sure that you are taking the lead in keeping the conversation going and finding avenues to help others and make it the society a better place for everybody.

[00:49:04] Thank you very much for talking about that. There's an excellent episode on Patriot Act, on Netflix. Have some AIs. I did a kind of a short episode immediately after the George Floyd incident, just talking about the just the lack of knowledge, the ignorance of the DC community around the struggles that black people have faced in America. I live in Canada now, but I'm from Sacramento, California. I was born and raised there. I didn't leave till I was in my thirties. And my family came to California in the 70s. And the only reason they got to California was because of the work done in the sixties and the Civil Rights Act of the civil rights movement. Like I can guarantee you, there is no Indian people fighting for our rights or fighting to get us like, you know, make these things possible for us. So a lot of respect to everybody who did something to the Civil Rights Act.

[00:50:00] Absolutely. Yeah. You bring up a really good point talking about the Civil Rights Act when we're saying, like when we look at the history, when we're talking about like women's rights, women got right because men give it to them. Black people got rights because white people gave it to them. So it has always been in the history that somebody with more privilege gave the permission to another group and was an ally to them, that they helped them, they supported them, and that's how they were able to overcome whatever barriers or whatever injustices that they were suffering at that time.

[00:50:33] So last question here before you jump into a quick lightning round, it's one hundred years in the future. What do you want to be remembered?

[00:50:44] One hundred years in the future, I want to be remembered as the woman who kind of paved the path for specifically women in her family to know that they, too, can pursue education and they, too, can pursue careers, and especially for moms. And I think in our community, a lot of the times the mindset is that once you are married, once you have kids, your life is basically whatever. You're basically you're just following your kids path and you're following your husband's path and your life is pretty much over. So I want to be remembered as where other moms and specifically in the D.C. culture, they can take examples and say like, no, your life is not over. After these things happen, you are still a person in yourself and they're going one more step further. I think I want to be remembered as kind of like a person who gave back to the community and who did good for the community and maybe in small parks opened up paths for other underprivileged communities.

[00:51:50] Thank you very much. Absolutely. Absolutely. Love that. That's a wonderful thing to want to be remembered for. So jumping into a quick lightning round, what are you curious about right now?

[00:52:03] Right now, it's so personal, something personal. So my parents recently moved from Ohio to Seattle. So right now I'm just really curious. I know this is probably not the answer you're looking for. I'm just really curious about their future. I have my dad's resume sitting at my desk and I'm trying to figure out how to edit it, what kind of jobs you can apply and things like that. I know this is probably not your job, but that's that's like I took four days off of work just to basically do this and think about them and how I'm going to help them settle in and physically make a home here.

[00:52:37] That's perfect. I absolutely love that. But what are you currently reading?

[00:52:41] I just finished a book which is called Essentialism, and I also finished another book, which is simple The World, and I love both of them. So essentialism is about basically I struggle with saying no. And it basically changed my mindset into thinking that saying no is not being rude. Saying no is you being respectful and actually doing a favor to other person as well because you are stuck in your own time.

[00:53:10] And simple fact wealth is about all about investment and working for your future, like saving for your retirement stuff. And actually max out my retirement account. After reading that, I invested some money as well in the stock market after reading that. So that was I think I feel like I'm more prepped for my retirement after reading that book. So I would highly recommend both of these definitely to check those out.

[00:53:37] What song do you have on repeat?

[00:53:40] Currently? I do not have any song on my date, but I do go into these kind of circles where I would listen to one song for a long time. Right now I'm listening to Not Your Barbie Girl by RMX. And that song is basically about like I'm not a theater type woman that you want. You expect me to be boyfriend and this is who I am. And yeah.

[00:54:10] So I would have to check that one out as well. So if you could have a billboard placed up anywhere, what would you put on it?

[00:54:20] Interesting. Well, let's be a little bit strategic about it. Let's say if I were to open a business and I want to if I want to like work, if I want my startup up, let's say I do a startup and I wanted to grow, I would. And if it's a tech startup, I would definitely want it to be in the Silicon Valley so I can attract investors. So I might start up and grow.

[00:54:44] That's against a picture of you on a billboard to invest in me or my business.

[00:54:51] It could be my business, too.

[00:54:53] So we're going to do something a little bit different that I've been doing the last few episodes, and that is the random question generator. So we go ahead and pull this up. And interestingly enough, I just noticed that this is coming out of Washington. So from your school, if anybody is listening and you've listened to watch these episodes. If you want to make me a random question generator in Python, I'd be awesome. That's a cool project idea anyways. Have you ever save someone's life with somebody?

[00:55:22] Saved my life. There was one time I was taking those cold medicines. You know how big those are. And things get stuck in my throat very easily. There was one time I took that pill and it got stuck and I couldn't breathe. And luckily my husband was home. So he gave me kind of like, I don't know what it's called, but he basically made me throw up and said.

[00:55:44] Scary, the Heimlich maneuver. He was like, yeah, what's something you learned the last week?

[00:55:52] That's a good one. Where did I learn? I learned so I was preparing for my talk. I learned a lot about biases in the artificial intelligence and machine learning algorithms. One of the very controversial and famous biased Emmel model is the compass scoring. So that was new to me. So that's something I learned last week.

[00:56:14] What's your earliest memory?

[00:56:17] My earliest memory is when my brother was crying and I was trying to soothe him and console him until I made him stop crying.

[00:56:28] The last one here. What makes you cry for me?

[00:56:35] It's I am very close with my family. So anything when my loved ones are in trouble, I get really emotional and also like them, the unfairness in the world, discrimination and things like that that make me really upset and I can't hold my tears sentence.

[00:56:55] How could people connect with you and where can they find you online?

[00:57:00] They can connect me through LinkedIn, through Instagram. I also have my own website where they can find me some those colored dot com and pretty much all of my social links are there. If you are interested in following my Data science blogs, you can follow me on Medium Gabeira, right Data science as well as career related blogs. So yeah, I'm pretty much on all the social media channels, but I would say LinkedIn and Instagram and medium are kind of like my Medbury.

[00:57:29] I'll definitely include links to all those in the show notes. So thank you so much for taking time out of your schedule to come on to the show today. I really appreciate you being here and sharing with us some really great insights. Thank you.

[00:57:44] Thank you so much for having me. It was a lot of fun and looking forward to staying in touch with you.