Naveed Ahmed Janvekar is a Senior Data Scientist working at Amazon in the United States. He works on solving fraud and abuse problems on the platform that impacts millions of customers of Amazon in the US and other parts of the world using Machine Learning and deep learning. He has 7+ years of expertise in the Machine Learning space which includes classification algorithms, clustering algorithms, graph modeling, and BERT to name a few. He is using Machine Learning and Deep Learning to solve multi-faceted problems. He has a Master's degree in Information Science from The University of Texas at Dallas and graduated top of his class and was awarded a scholar of high distinction and inducted in the prestigious International Honor Society Beta Gamma Sigma. He has a Bachelor's of Engineering in Electronics and Communications from India. He has worked with other influential firms such as Fidelity Investments and KPMG. In his current role, he is researching identifying novel fraud and abuse vectors on ECommerce platforms and using Active Learning to improve Machine Learning model performance.
Transcription:
[00:00:00] Naveed: I'm not exaggerating this, but I think I applied to more than a hundred companies. To find my first internship because even finding internship in 2015. So that's when I finished my first year of master's. It was not as easy as it is as accessible as it is today because data science was still relatively a newer space and not a lot of companies are acknowledged the fact of the value of data.
[00:00:34] Ken: Today, I had the pleasure of interviewing Naveed Janvekar. Naveed is a Senior Data Scientist working at Amazon in the United States. He works on solving fraud and abuse problems on platforms that impact millions of customers of Amazon's in the U.S. And other parts of the world. And he does this using machine learning and deep learning.
He has a master's degree in Information Science from the University of Texas at Dallas. And he graduated at the top of his class and was awarded a scholar of high distinction. He was also included in the prestigious international honor society, Beta Gamma Sigma, which I also happened to be a member of. He has a bachelor's of engineering in electronics and ,communications from India as well.
Now he's worked with other influential firms, such as Fidelity Investments and KPMG. And in this episode, we learn about how he managed the culture shock for moving from India to Texas and how he believes the data science educational system could be reformed. I really enjoyed talking with Naveed. I hope you enjoy our episode.
Naveed, thank yoU.S.O much for coming into the Ken's nearest neighbors podcast. You know, we talked a little bit and you have such an interesting story coming from India, doing your master's degree in the U.S. And I landing a job here. You've obviously been at a fan company for, I think it's almost five years now, and I'm sure that you have some incredible learnings from that entire experience that I've just illustrated.
I'm so happy we could share your story with everyone today. I'm again, certain that they're going to learn a lot from the story you don't hear. So thank you again for coming on.
[00:02:00] Naveed: Yeah. Thanks. Thank you again. And first of all, thank you so much for giving me this opportunity to ... know about my experiences I've had so far.
And definitely, I believe that some of these experiences will help the audience watching those channel and then this video, so excited to be here and looking forward to our conversation.
[00:02:20] Ken: Incredible stuff. Well, so the first thing I usually like to ask every guest to get familiar with, you know, their story is how did you first get interested in data? Was it a pivotal moment that something happened or was it a slow progression over time.
[00:02:35] Naveed: Yeah, I would maybe dumb it as a scheduled moment, rather than a pivotal moment, but sort of progress from there. So this was in 2013 and this was right after my graduation from engineering. So I got into this job at Fidelity Investments as a Java developer.
And the company, I was mainly developing applications for content management for the phone. But I also had a couple of friends that are financial analysts working with a lot of data. And then gathering insight and solving some of the business questions or problems at that point that I would just casually have a discussion with them, trying to learn more about what they're trying to solve.
And they will basically say that, Okay, that analyzes data, come up with different visualizations, trends, charts for the business leaders. To take some decisions that will definitely impact the company. So, this was sort of my moment that I thought that, Okay, this looks interesting. Maybe I should more into this, and try to figure out what data analytics and data science is.
So when I started doing that, just to see if this is something that feelings that me, because at that point I was also thinking of what do I want to do next in my career? Because I completed my engineering. I got this job as a Java developer, but I would still say that was a spot that was missing in my life.
So I was still sort of exploring things to do in the future because I also wanted to pursue a master's as well. And at that point I was a little confused as to where do I want to take my career? Do I want to do a master's in electronics, which was my undergrad degree? Or do I want to do something new, maybe a combination of business and a little of technical?
So what I started doing was I. Also wanted to analyze a lot of the spend that was happening. I wanted to see, like, where is the money going? What are the different types of transactions that we are making at home? It's just sort of understand where, where is the money going that that's being spent by the family.
So what I started doing was basically logging all the transactions in an Excel. So Excel was my best friend at that point and I started logging. Okay. So this week we had these, transactions. And these were the areas where me and my other family members are spending on and then on a monthly basis, I was just drawing basic visualizations and then just doing some trend analysis and, you know, basic pie charts and histograms, just to see, Okay.
Maximum amount of money that we're spending is maybe at the movies or at the restaurant outside. So that was sort of the moment where I knew that, Okay, this is where I want to pursue my master's and maybe utilize the same kind of techniques used the data and various companies to maybe solve their business problems, because I definitely knew the value of data that point and wanted to sort of have more expertise in this space.
So that I would say was the moment when I realized that, Okay, this is what I want to proceed for the in terms of specializations or master's. So that I would say would be my moment.
[00:05:57] Ken: I really like that. So the analyzing your personal finances is something I've heard before. It's one of the best first projects I think anyone can do because it is directly relevant to your life.
You get to see the pure, clear impact of how data can help you save money, which a macro level is what you're doing for a company, right? Is they're either helping them cut costs or you're helping them. Find different revenue opportunities or whatever it might be. And I really liked that. That was one of your, your first ways to sort of foray into this data domain.
What about that? Did you like so much and what were the next steps after that? Where did, where did that analysis.
[00:06:35] Naveed: Yeah. So, that analysis at least gave me some clarity into what I want to do next in my life. Because like I said, I was also planning on pursuing a master's a master's degree. And I was really confused whether do I want to go into my electronics, or maybe something data related and then sort of clear that I wanted to go into something data related.
And at that time, UT Dallas university of Texas at Dallas was offering a very nice course. Which was a blend of a bunch of business courses and machine learning courses. And that I would say was a best fit for me at that point in time, because that's something that I was exactly looking for. And then I applied to the university and got selected, and that sort of led me to the next part of my career of doing, doing a master's degree here in the U.S.
[00:07:27] Ken: So how did you find out about that program? Was it just a bunch of Google searches?
[00:07:31] Naveed: Yeah, it was just a bunch of Google searches. And at that time, I wouldn't say I was too familiar with data science as well. So I would just search a bunch of keywords, like data, like data analytics, business courses. There's a bunch of relevant keywords. And then Google was my friend there.
So UTD sort of came to the top. And I had a couple of hundred universities that I also applied to but UT Dallas was the one that I chose.
[00:08:04] Ken: That's awesome. How did you decide, or how did you know that you wanted to pursue a master's at some point, you know, it's just like, Hey, I like school or what went into that decision?
[00:08:15] Naveed: Yeah, so, I would say initially it was a lot of insistence from my family because my parents they like any Asian parent, right. Or an Indian parent, definitely one that gets to do a master's, do a PhD. So there is a lot of insistence from my parents that, Okay. Hey, you got to do maybe a master's and somewhere, somewhere abroad. And basically the reason behind that was they wanted me to experience how the world is outside of India.
And because they themselves migrated from a remote part in India to a major city in India, which is Bangalore. And now they wanted me to go from Bangalore to a different part of the world and experience the culture education. And basically gathered that experience and they were okay with the fact that, Okay, even if you don't work there, don't find a job that you can still come back to India, but we at least want you to go experience different cultures and learn different education from different people that may not have the same kind of background for a firm of people from India. So, that was my inspiration or motivation to come to the U.S. And I was actually supposed to come to the U.S. And in 2013 that was the plan right after my undergrad to my master's.
But I never moved away from Bangalore my entire life. So I was a bit homesick and I actually pushed out my master's by about a year. And in 2014, that's when my, when I came to UT Dallas for my master's. But yeah, going back to your question it was my parents insistence for me to pursue my master's. But then, yeah, I think it was one of the best decisions of my life.
[00:10:04] Ken: That's awesome. I think that that's such. Cool bit of wisdom from your parents. And, you know, I think that that's incredible. I sort of leaving the nest and charging our own path and seeing what the world is like. I think, I think it's a very important thing for everyone, whether it's leaving your city or your hometown, it could just be going to the city, but it's still spreading your wings and you learn so much about yourself in those experiences.
So I have two questions. So the first is, you know, why did you choose the U.S. Over for example, And then after that is going to be about your experience in Dallas, which I would imagine is very different than Bangalore.
[00:10:41] Naveed: Yeah, yeah, absolutely. Yeah. I think a couple of reasons why I chose the U.S. over Europe. One is definitely the advancement of data science in the U.S. versus Europe, because based on my research, I did find that, Okay.
U.S. has these many universities that are offering data science related courses. And these are the available jobs in the U.S. versus Europe. And as well as the work with our flexibility. So us that time when I came, had. The option of three years of a working design in the form of OPD, that's called the OPD.
Right after your graduation, that you can still work in the U.S. even if you do not have like a H1B on visa and versus Europe, I did have some challenges in getting work visa and a lot of my. A lot of the people in my neighborhood in Bangalore were already in the U.S. and they would always tell me so many good things about the U.S.
So I would say it was a lot of influence from the people in my neighborhood, as well as the available job market that led me to, and definitely the availability of courses and university that led me to choose us over Europe. Yeah.
[00:11:57] Ken: Oh, that's awesome. I really, you know, those are the things that I hadn't considered.
So, you know, the one, the one thing that. I know about us versus Europe versus some other places, frankly, the U.S. graduate degrees and programs are very expensive compared to the rest of the world. How did, how did that factor into your decision? I mean, do you have any advice around that? I mean, if someone's coming from Bangalore, frankly, the exchange rate.
It could be exorbitantly expensive. What are your thoughts on that?
[00:12:26] Naveed: Yeah, definitely. It's a pretty challenging situation to be honest. And I know a lot of my friends who could not pursue an education in the U.S., just because their finances are not not as good as they had to be to even get a visa.
So they had to wait a couple of years until they made that kind of money or maybe take a loan. But I would say I was fortunate in that they got, because at least the first year my family funded my education. I think every semester it was up coming up to like $12,000 and we had about four, a four semester. So roughly about $48,000, $58,000, it was just the tuition fee. And plus.
You're definitely looking at about a $60,000 to $70,000. Yeah. So that's, that's a huge amount. A lot of people are coming from countries like India or that part of the world, right. Where families are not owning as, as much as that. So first year, at least my family supported me. I had the necessary funding to fund my education, to get, get the visa and the.
Luckily in the second year because I had like a 4.0 GPA and across all my subjects in my first year, I got a deans excellent scholarship that made my tuition fee in state and that straight away we have dolls, like 50% of the fees that I have to pay. And then I also had an internship at the end of my second semester that helped me find my second year of education.
So, that's how I funded it. So partially my family's farm, the money that I earned on a internship and the scholarship. So that helped me bring down my overall fusion plus living expenses considerably. So I would suggest, I mean, anybody who is trying to pursue a master's definitely try and apply for scholarships.
I know there are scholarships, scholarships available, even before you come to the U.S. So definitely you one could try qualifying for such good scholarships and also not all universities are expensive at the same level, some universities do have like a tuition fee and then there are low cost of living cities.
Like Texas is pretty low cost of living compared to maybe Boston or New York. So yeah, but that's the way I funded my education. But my sedation would be trying to apply to as many scholarships as possible where one would qualify..
[00:14:55] Ken: I really liked that. I think people don't realize how much effectively free money there is out there.
This was a while ago when I was in college. I forgot. I think it was like the some random scholarship you just had to write an essay for. And you know, it was, it was a sizable amount of money. It was like $4,000 or $5,000 per first semester. And I was one of like three people. That wrote an essay and applied to it.
Right. And so I ended up getting it, but if you look at all the opportunities that are out there, you might be surprised. It just other people aren't looking, or they don't know what's available, like doing your homework on the scholarships, because you'd be surprised at what you could find.
[00:15:40] Naveed: Yeah, absolutely. A lot of it is actually lack of information because there's so much out there, but I wouldn't say everybody knows. What all is available and is this doing research to find the right kind of opportunities out there and just tapping it into it? Right. So, yeah, definitely I'd encourage people to just, you know, spend some more time into sorting out the finances.
And once we land here in the U.S. Maybe after a year, you're definitely there a lot of internship opportunities that you can own money.
[00:16:10] Ken: Yeah, I will say that as a huge benefit of data science, software engineering, tech internships is the pay is usually very good. And sometimes even house you there's a lot of benefits associated with that. So I'm very interested now. So what was your experience in Dallas? Like you know, I have to imagine it was a major culture shock.
[00:16:34] Naveed: Yeah, so I think the first shock had, was with respect to the weather because I landed in Dallas. I think it was in August. And the temperatures go up to like 90 degrees F. So that's the sort of temperature range and Bangalore is typically in the seventies. So I think that was the first major shock as soon as I landed, that was so hard. And then at that point I didn't even have a car. So I had to like walk around to a bunch of places and Texas public transport is not that great.
So definitely a challenge in terms of weather. The second aspect was just coming away from family because this was the first time. That I left my family, left India and came to a different part of the world where I didn't know anyone. So that was the second challenge in terms of being in a place where you don't know anybody that well, that you can count on.
Difficult situations in terms of the other aspect was definitely the food. I think I had some challenges because I didn't know how to cook and being a student and eating outside can be really expensive. Although there are a lot of good Indian restaurants in Dallas. But again I didn't want to like add on to too many expenses that. So, I would say I have to learn cooking because I didn't really enjoy the food as much as I'd like.
And then I do eat a lot of tacos and pizza. So, that was the first sort of shock because in Bangalore, india, I never had that much pizza, or backwards. And in Dallas pretty much every other day I was having. So, yeah, it takes me. Yeah. But yeah, now in Texas, tacos and pizza are one of my favorites, but yeah, that was a shock in terms of like weather and food, but in terms of people and blending with the crowd I think it was not as difficult as I had imagined and the reason being UT Dallas has a pretty big Indian, Asian population.
So I could still find a lot of people who are from Bangalore or India and then make friends with them. And even the locals in Texas at least the ones that I met were pretty friendly and they, they welcomed me with you know having an open mind to understand and accept any cultural differences that may come across during conversations.
So I wouldn't say I had like a major cultural shock in terms of meeting people or. Going ... locals, but it was definitely a shock with weather and the food. So that was my experience. But yeah, I was fortunate to make a bunch of good friends. And then through a bunch of like mutual connections, I did find out like, Okay, my distant relative lives in Dallas.
So I went and met them a couple of times. So yeah, this was my experience in the first few months. And then I just got so busy with college and studies and in finding a job that. None of these things mattered much because my next aim was okay, how do I find an internship? How do I find my job? So, yeah, that was sort of my experience as soon as I landed in Dallas.
[00:20:05] Ken: That's awesome. And so how did you find your internship?
[00:20:08] Naveed: Oh yeah. It was pretty hard I think and is, I'm not exaggerating this, but I think they applied to more than a hundred times. To find my first internship because even finding internship in 2015. So that's when I finished my first year of master's. It was not as easy as it is or as accessible as it is today because data science was still relatively a newer space. And not a lot of companies acknowledge the fact of the value of data that we'll bring into a company. So it was still a fancy term.
People were experimenting with the role and very, a few companies offering that kind of role. So I applied to a bunch of data analysts, data scientists, business analyst, a technical analyst system. And is any, any job with the word analyst in it pretty much had any, anything to do with data. So I applied to about a hundred companies.
I think maybe like 10 companies actually even like responded back as they enter. Okay. We would like to move ahead with with you. And then I think I can learn about two or three companies interviews and got the offer. But one thing that really helped me get my internship or some of my experience in the analytics.
So my elder sister, she had a e-commerce startup back in India where she was selling India. On the internet. And I did a bunch of web analytics using Google Analytics on her website. And basically made a nice project out of it. And then showcased how I helped her company grow based on the insights that I was getting.
So I do that, give an idea to the company that was hiring me at that time that, Okay, this guy knows what to do with data and coming up with metrics and how those metrics can relate to the business. So that really helped me to get my first internship and the source at Nanigans. It was a Boston based tech startup. And there was primarily working on Facebook Ads. Now it's acquired by a company called Sprinkler, but that was my first internship in, in the U.S. which was in Boston. And I was pretty excited about it because. Getting to go to Boston, which was a completely new city and new experiences and my first ever internship experience in the U.S.
So, that was good. But yeah, this is how I got my first internship. And again, one other aspect that I would like to share is a modifying resume. As for these different companies. So I literally had to modify my resumes so much that because I wanted my resumes to be well aligned with what the companies are looking for.
Because a lot of times you have these internal systems that are doing maybe keyword matching. So I just wanted to increase the probability of my resume, at least getting. So I to work on my resume and multiple times even go to the career development center at the university, get a feedback on that as you may get the thoughts, do mock interviews because again, interviewing here in the U.S. is different than interviewing in India.
So I wanted to get as much practice I wanted as I wanted. So UT Dallas has a very good career development center and they do mock interviews, resume reviews. And even help with networking to a large extent. So all of those things help, but yeah, I think I sort of maximize my chances of landing an internship by applying to tons of places.
[00:23:39] Ken: That's awesome. And so, you know, you mentioned that job interviews are different in the U.S. and in India. In what ways are they different?
[00:23:48] Naveed: So, typically in India, once we graduate from maybe a bachelor's degree there is something called campus placements, where a lot of companies actually come to you.
It's almost like a, it's sort of a career. It's a career fair basically. Where you have these different companies and thousands of students actually write the initial and we call it like an entrance exam for these companies where you have like a bunch of basic questions. Like a first level of screening.
And then once you clear that, then you get assigned to like tier one companies, tier two companies, and then you go through the interview process. So, there is this career fair that the companies go to in different colleges. And then you have a chance of like hearing those interviews. But here in the U.S. I found that the career fairs are not as many they are in India. There is not much of a concept of a campus placement. But rather there are career fairs, but it's not as I would say in some sense, effective because at least in India, you would find at least one single job in maybe a tier two or tier three company. So that was at least a guaranteed, right?
If you have completed your bachelor, then you have these consulting firms are simply hire you. So, that was one different that I found. And apart from that, it's just around the. Yeah, that I would say would be the major difference apart from the technical aspects and the technical ones are similar, but just getting access to those companies is different in India versus here.
[00:25:21] Ken: This episode of Ken's Nearest Neighbors is brought to you by Z by HP. HP's high compute, workstation-grade line of products and solutions. Z is specifically made for high performance data science solutions. And I personally use the Z Book Studio and the Z4 Workstation. I really love that the Z line can come standard with Linux and they also can be configured with the data science software stack. With the software stack, you can get right into the work of doing data science on day 1 without the overhead of having to completely reconfigure your new machine. Now back to our show. It seems like in the U.S., it's sort of broad strokes. You have to go out and get a lot more.
[00:25:58] Naveed: Yep, absolutely.
[00:26:01] Ken: Well, I think that that's a really important thing to notice about the degrees that you pursue as well.
Is that when you're doing it right. Right. When you're doing a master's, when you're coming to the U.S. for education, it's not necessarily what you do in the degree, just getting the degree guarantees you, literally nothing, which is unfortunate, but you really have to be proactive in leveraging the resources like you do.
Like you did reaching out to companies and trying to find opportunities for yourself because that's what effectively pays the most dividends is the stuff that you do outside of the classroom rather than what you in the classroom.
[00:26:39] Naveed: Absolutely. And I think tools like LinkedIn is very useful a lot here in the U.S. So I remember even during my internship search, I reached out to a bunch of recruiters and managers who were hiring in terms of that.
And then just sending him, I took all LinkedIn Premium packages that time, and then just started in mailing people. And I think the response rate was maybe like 5% or 10%. So that's another thing where I would say you have to be more proactive in terms of reaching out to.
And then at least putting yourself out there and then at least letting them know that, Hey, these other things that I've worked on. And I think these are the ways I think my skillset is applicable to the problems that you're trying to solve. So I think putting into that way would be useful.
[00:27:25] Ken: I really like that. And you know, something I do want to be sure to note is that there's a right way and a wrong way to reach out to people and be proactive. I think I got it a lot. Just people spamming messages. You have a worst chance, spamming a message then going through a traditional path. Right? If you do send a very nice personalized message, you send a message that you would like to receive in your inbox. You have a reasonable chance of getting responded to, but the volume game does not work in one-on-one conversation.
[00:27:57] Naveed: Yeah, absolutely. It's yeah, it's never a good idea to just make a cold ask, right. Without giving some background around what you're doing. And why do you think this message is even valuable to the other person who's reading it? Because I think you need to structure it in a way that not only just benefits you, but the other person, because you've got to respect their time as well. So, yeah, definitely. I agree with personally,
[00:28:20] Ken: Awesome. And so one other question I had, I was sort of thinking about it as we were talking about master's degrees. I understand the benefit of going in person to your degree and your family wanted you to see the world and explore different things. Do you think it was really necessary to go in person? I mean, I did my master's in computer science. I started in person and then I, for the last year I did everything online because I was like, you know, why am I going into class when I can do all this from my home, but what's the trade off there. And what is your experience?
[00:28:55] Naveed: Yeah. Yeah, that's a great question. I think situation is different now because, because of COVID and very limited opportunities in terms of like in-person, but I still, at least my preference is still going in-person to the university or college and meeting with your friends and discussing.
Subjects and topics as a group, because a lot of times when I've had struggled with any topic or any subject, the ease of accessing a professor, or just meeting them in a corridor and getting my doubts clarified. And there are making a study group and studying in a lab in the library was very effective in terms of getting those, those doubts clarified.
But as now I feel you know, even if you have to like each or the person, you need to write an email, wait for the response. And you know, sometimes you even shy from making such an email. Because again, you need to sort of think about, do they have the time for it, but maybe in college or just.
Walking in a corridor, you meet them, you have a casual conversation. So I think those are the aspects that I really miss. And the other aspect is just networking because again, in in-person maybe like, for example, the career fair you would meet like a lot of resources, people, maybe recruiters, people from different companies in the same space and the effort there would just be like a one time effort where you just meet a bunch of recruiters and people who are there or meet a bunch of professors.
You're ... to maybe submit your resume, but now in the online world, I think you still have to make those efforts individually for every person that we want to reach out to. So definitely miss the sort of the human aspect. That's there in the university versus online. So I would still say that there is benefit in terms of going in-person to the class, making a friends group or a study group, and then do studies together or projects together.
But again, your times are different now. So people are still trying to make it work. I actually attended the. Oh, virtual career fair. Just to sort of see how that goes. It was sort of created in the environment where you could actually game-ify yourself walk to different boats. I even, that was fun. So I think people are trying to digitize those offline new ones in the online world, but I still see that I still feel that there's a lot to catch up and I don't know if we will be a hundred percent be able to replace the offline. And the online world. But yeah, those are, those are my thoughts in it.
[00:31:32] Ken: I like that. I think something, I personally got the best of both worlds because I lived right next to the school and I could go and see the professors. I could go and do whatever I wanted. I just didn't have to go into the class.
And all my classes were between, I think it was 5:30 and 9:30. One night a week PM. And so I'm not an evening person. I'm a morning person. So I'd write the next day. I'd watch the lecture. If I had questions, I'd go in the office out on it. And that's something I think is really important that you just described is kind of leveraging the resources of the university or of the educational institution.
So you talked about how you use the. You talked about how you helped with that help with networking. I'll be saying something that people don't do. And it baffles me why they don't knew us is leverage the office hours of the professor has study groups and these types of things, you got the professor who is teaching you the information you can get 15, 20, 30 minutes of their time.
They tell you exactly the answers to your questions, right? It blows my mind that people don't leverage that as such an incredible resource when they're in school. And I, you know, I had good relationships with my professors that presented job opportunities. It presented research opportunities. I didn't take any of them, but they were offered.
And so to me, I don't know. I just think that's such a. To not use those resources, if you are pursuing this degree, I mean, you're paying for it. So.
[00:32:53] Naveed: Yeah, absolutely. I think like you said, office hours is a very effective way of I mean, going beyond just the syllables that start in the classroom, right?
Because like you mentioned research areas, I think that's very important for people who are pursuing a career in data science or machine learning. Because it's not easy to just access to any research group. But who's doing like some really relevant or cutting edge work. And I feel professors have access to these resources are here. They can point you towards various research groups that are actually doing innovative stuff, or maybe even they can make you part of any of the papers that they are working on for publications, citations. And then apart from that, I think networking because. Even, even today I have like good connections with the professors from my master's.
And those have typically been developed during office hours. Just clarifying additional dollars because you just cannot get everything clarified within, within the one hour classroom session that you have. So I've made use of these office hours and I think it's yeah, one of the benefits of online versus offline. I'm not sure if professors are doing offline. I mean, online office hours.
[00:34:06] Ken: Okay. And no, nobody came. I was like, Oh, they came when they, when they were struggling with their projects, but not pass the question. I remember I had one professor, my first machine learning course, and I would go every week just to ask questions about my personal projects.
Just ask questions about things that I didn't understand in the field. And you know, the guy bless his heart. He would sit there and listen to me. And, you know, I consider him a friend now we haven't talked in a while, but I should probably reach out. Yeah. So moving on from, from your experience at school, I'd love to hear about landing your first job and you know, what that process was like for you, what you learned about yourself and...
[00:34:49] Naveed: Yeah. So, once I came to, so yeah, Fidelity Investment, Java developer, that was my first ever job in India, right after my master's right out from my engineering, bachelor's in engineering. And then came to the U.S. and then, Nanigans so it was my first internship that I was working as an Analytics intern and primarily the job was around analyzing customers ad spend.
And basically looking at ways how to optimize that ad spend, right? What actually plan, what actions actually translate to a good click through rate or good impressions. So a bunch of regression type of analysis that I had to do for the company. So that was in that internship phase. But yeah, my first full-time job here in the U.S. was with KPMG which is a big four accounting firm and I got this job. Actually that's part of my, part of the career fair that happened at UT Dallas. So you had a bunch of companies from like KPMG apple and a bunch of other companies that came that had their stalls. And then this, I happened to take one of the Wizarding cards of the recruiter and then, you know not spammy mail them, but yeah sent a nice note saying that, Okay, these are the areas that we had discussed that you are working on, and this is how I think my skill set would fit for your company. And then I got a call from KPMG to for an onsite interview, actually I in Dallas and then that interview went well and I got the job offer, but their responsibility is their role.
Mainly in the business intelligence space where I was developing dashboards, using tools like ClickView to come up with metrics and visualizations for small, the internal reporting needs. But again, this was although it was good work, but it wasn't exciting enough for me to sort of stay a.
Yeah, the company for too long. And that's when I started looking out for other job opportunities. And I came across this analyst position at Amazon in the fraud and abuse prevention space. And I had a few friends who were working at Amazon before I joined. And they would tell all these nice things that they are doing at Amazon, so that and definitely Amazon being a top tech firm that was always the motivation to be part of that firm.
So I joined as a business analyst for Amazon and worked for about a year as a business analyst coming up with metrics and reporting stuff dashboarding and then I think it was after about a year, I got my first, a machine learning project where I had to build a classification model for one of the abuse vectors that we're seeing in the platform.
So that was my transition into the data science jobs, family within the company. And then, yeah, I think after that, it was just delivering impactful machine learning projects that had a sizeable business impact that really mattered to the company and yeah. Being with the company now for about eight years as a and then yeah, currently the senior data scientist. So that's been my transition ever since my undergrad.
[00:38:09] Ken: That's awesome. And you know, something that I'm curious about is how did you get that sort of first machine learning project? Could you have to advocate for it or did it just sort of come to you?
[00:38:18] Naveed: Yeah, I would say I was it was a lot of advocating to be honest, at least at the beginning because it's not the usual norm to give a machine learning project or to a business analyst because I had to definitely show or showcase how my current skills.
It's even relevant to the problem that's that needs to be solved. So even as a business analyst, I worked on a couple of machine learning projects that I did like show Intel to other folks, other scientists within the company. And then there's a whole process internally just to sort of do a validated check on the skillset before the official transfer happens.
So yeah. So a lot of fun with Casey and being able to like, prove my skillsset. That, Okay. I think I know, I know what I'm doing, and this is all my skill set is going to be applicable to this problem that that is being solved. So yeah, that's how I went about. But yeah, I made some good connections with some of the scientists that point who also were my mentors and gave good guidance into how I can be successful in this space. So all of that helped.
[00:39:26] Ken: I really like that element of the story because. I mean, honestly, that's something that I recommend is the easiest way to transition into data science is to have a analyst or the job and solely accumulate projects and portfolio and do it within a company rather than going externally.
I mean, people talk about how hard it is to land the first data science role. Yeah. In theory, you don't have to land your first data science, right. You land your first analyst role and transform it. And then after your, after someone calls you a data science. Yeah. It's really not that hard to catch, which I think is very fun.
[00:40:04] Naveed: Yeah. Yeah, absolutely. Yeah. And I think also one other good thing as, as an analyst or a BA, is that you get very comfortable with data and then you would know, Okay, this is the sort of layout of the data ecosystem within my company. And this is where all the tables or the fields, the fields reside. So getting that comfort level, I think is very important because even when you become a data scientists you don't let go of the data work. I need feature engineering. You need to tap into the right data sources. So I would still say like an analyst role in sort of building your strong foundation around. That then you can become a data scientist and leverage these skills that you've developed as an analyst.
[00:40:45] Ken: I really like that. I mean, the skill sets are so parallel or I wouldn't say parallel it's a continuation, right? I think the analyst is more of the beginning of the pipeline and data scientists. Yeah, well kind of the whole pipeline, depending on where you're working or working for you know, something related to work over, you know, over the course of your career. I think I'm interested in if you should specialize or not, you know, in a sense when you work on a specific.
Right. You do specialize within that domain. Yeah. You know, do you think that you've specialized over your career, particularly damage. Or do you think of yourself more as a general generalist?
[00:41:32] Naveed: Yeah. Yeah. That's a great question. I think overall industry wide, the definition of a data scientist varies company to company, but broadly people view a data science scientists as a more sort of a generalists who can pretty much do most of the things with, with data.
But I think personally for me, that is my opinion is that being a generalist is good, but having specialization and maybe one or two specific areas could be beneficial. And for me, it has been a large influence because of the work that I've been doing at Amazon, because in my current role, I've been mostly working on classification type of problems, and then doing a network analysis graph related work.
And I've been sort of specializing in that domain and that also interests me. So I think if there is any particular areas where you think you really liked that space, maybe it could be a forecasting or it could be immigration type of problems, because I think once you are in a particular specific area for a longer time, you will know like, Okay, what are the things that are.
What about this idea or what are the problematic areas? And we can do further research or, you know, maybe write some research papers in that specific area, or even do some inventions, right? Get, get some technology patented in that area. So although I think being a generalist is skirt, a specialization specializing in at least one or two specific areas is different advantages.
And a lot of companies also, I mean, eventually people want to keep getting good job opportunities as well as they proceed. And a lot of companies have specific roles in specific areas, maybe in forecasting or maybe in classification or regulation. And sometimes they look for specialists in these areas.
So that will also give you an added advantage rather than just being a data science generalist. And that's, that's my opinion.
[00:43:26] Ken: I like that. I think I hear a lot of people talking about sort of a T-shaped. Yeah. Still attributes where you're, you have a fairly broad range of skill, but you specialize in a couple of different things that I think it's almost exactly what you're describing and you know, the more specialized you are, the higher chance you have opportunities in those specialized areas, right?
The more general you are. You're not getting that same advantage of having better opportunities in more areas, you have more opportunities in theory, but the quality of opportunities and your qualification for all those opportunities sort of goes down sort of that min-max idea associated with the job market where you don't want to be the 75th percentile of every exactly.
Right. Exactly. Yeah. You want to be there the top, top 1% or cares? Yeah. So I think that's a, you know, it's a very fun and interesting sort of concept to, to wrestle with. Something I'm interested in, as you progressed through your career when you've been in the U.S. For more than three years now. Yeah, exactly.
Three years is the, is the exception that you get with education. Well, you know, I assume you were planning to stay in the U.S. For a while. What does that, what does that look like? What was the landscape after your three years?
[00:44:48] Naveed: Yeah. Yeah. So, yeah. So once you come home, come to the U.S. As a student on a a F1 visa, and then right after you arrest and visa, your master's is in a stem field.
With math engineering some sciences you get three years. Work with in the name of opp. And then while you're on that OPT visa, you need to get a H1B visa for continuation of your work. So you get your hedgemon visa. I mean, it's a lottery system. So every year there are only so many that are allotted and you need to you need to get your application picked up in that.
And you have three attempts at it. Basically so the first year you get your OPT, you have an attempt to get your hedge fund, big, different amount, get it big, you get it in the, you try it in the second attempt. Then the third attempt. So I was fortunate to get it picked in the second attempt. I didn't get my big in the first attempt.
And again it's just not, you, you have people also competing from. We'll want to directly come on H1B visa in this pool. So by having a master's gives you an edge, because I think that is a specific quarter for master's students who have done their master's here. And I do know a lot of people who didn't even get that, it shouldn't be in the total lottery and they had to leave the country go to a different country.
So so yeah, there's a bit of an element of luck that's involved in getting your application, fake footage and once you get your audition, we typically get it for about five years. And then you have to keep renewing your H1B until you get your green card. But yeah, again, the green card process is is pretty long.
That is that's skill for Indians because there's a huge backlog. And as per current estimates, it can take a few. Before an Indian could get their green card through the so again, in grade card you have like multiple categories like EB-1, EB-2, EB-3 and EB-2 is the category where master's students mostly fall into.
And if you have like a PhD or a, if you're basically like a top one person in your field, you would get into like an EB-1 category, which is faster than the other categories. But typically most people fall either in EB-2 or EB-3, which has like tickets with them. And then until you get your green card, you have to keep renovating your H1B multiple times.
And that is not a good situation to be in because again, your H1B is tied to a specific company and then you cannot do anything of your own apart from that job, maybe if you want to start a company, start a startup, you're limited by options. So that's the challenge. So for Indians, I think, yeah, most of the people, you know, have to wait a long time for, to get their green card.
[00:47:36] Ken: Awesome. My friend, Dhaval who runs the codebasics, YouTube channel, he recently got his green card and now we can actually start monetizing that content we've been doing it all completely for free until today, which I think is incredible. So yeah, I mean, it's an interesting constraint. I think it's really important to think about.
There's also. And if you're coming from different countries outside of India, there's different constraints on H1B. So if you're coming from Singapore, for example, the quota for Singapore, they almost never hit. So it's significantly easier for some countries to work in the U.S. I've heard. You know, in terms of working in other countries, Europe has a lot of countries in Europe, but it's generally a little bit easier from India.
And I think that, you know, that is an, the kind of meta analysis that you might want to do when you're looking at pursuing, working in a different, in a different country or working globally. But you know, that is, you know, kind of one of the unfairities of the world. But it is something you can study and understand and figure out how to give yourself the best..
[00:48:38] Naveed: Yeah, yeah, yeah, absolutely. I mean like I said, there are like other ways of getting green card well, for Indians. So we just have to meet the criteria for those other categories or work toward that criteria. But, but yeah I think it's just a process that we, we need to follow
[00:48:58] Ken: Very cool. And so, you know, one thing I wanted to end on, we talked about your awesome career, your incredible learnings or education, but we've also talked a little bit offline about how you think education in the data space could be improved.
I'd love to hear your thoughts on that and get maybe a little bit of insight into how you would, you would make a better education system.
[00:49:19] Naveed: Yeah, sure. Yeah. I think absolutely. I mean, I'm in currently I think the, I think the good aspect is that. A lot of bases that are offering a data sense education and most of the places are doing a good job and you know setting the foundations, right.
Getting the right skillset to at least be like a data science generalist. But I think one aspect that I see missing across a lot of these platforms is that not enough emphasis on specialization, like we talked about that maybe at least from my practical experience, I think once I spent.
Two or three years in the data science role, I wanted to get more involved in research areas or, you know, making some data science related inventions, getting those inventions patented, but it was very hard to find groups that are outside company that are working on some data science problem that's that they're trying to solve for for the industry.
And that's one area where I feel that these ad tech platforms can have some sort of a research focus groups where people can publish. And then contribute their skills to those research. And eventually with a, with an objective of getting an invention patented, or maybe publishing a particular paper, that's one area.
The second area is around mentorship and guidance. I see that maybe there are a couple of platforms that are getting industry experts to mentor and coach a data scientist, but I would like to see a lot of other platforms to the same where they, you know, you have like maybe a director of data science who is coming and giving their perspective and guiding young data scientists who might've just joined that first job as the data scientist, or even like students were just trying to get into the field and maybe set the path right with his without the mentor or a guidance yours, I would say to some extent you might be lost in terms of what is the right part for you, but at least having someone guide you and to look at these are the opportunities of it.
And sort of setting that phone part for you. So that leads you can consider to proceed on that in that's another area where I think platforms should do somewhat effort in terms of making that more mature. The third aspect would be generally in terms of making you more competitive in the space.
And what I mean by that is most of the data scientists have more or less similar skillset, right? Maybe they all know how to take an off the shelf library. Make a model get it up and running, do some product sends product intuition. But how many of these can we actually, you know, push the limits, make them like the top data scientists in the world?
Like, what are those things that will actually make your top in your field? Maybe? Is it through research or is it through making some presentations, attending conferences? So I think these are the areas where I think platforms should still guide a lot of young professionals and stuff. To consider when, when the maybe studying or even there are leaders in that profession.
[00:52:27] Ken: Awesome. I think that those are all incredible insights are ways that we could improve and really make this, this field more progressive. And something that I realize about myself, right. Is that. I'm probably never going to make the most cutting edge, incredible data science startup, or a technology company or whatever it is, right.
That's not my role to play, but hopefully the content that I create, the stories that I tell, the people that I interview will inspire someone to be interested in this field or help them to make progress in this field so that they could. The person that does make that incredible advancement. And I think that that's so cool is that in some small way that I can be a part of that, hopefully, you know, there are plenty of other great educators that they're probably going to listen to hers before, but you know, at the same time, I think that there's, there's something very powerful about that.
And, you know, teaching is education is what drives innovation and will strive to change, especially positive change. So I really admire that how. Learn more about you? Where can people get in contact with you? What's the best places for that?
[00:53:37] Naveed: Yeah, I think the best place would be LinkedIn. So I guess we can share my LinkedIn profile and the video, and then people, they want to have any further conversations. They can reach out to me on LinkedIn and I'd be happy to connect and help our guide in whatever way.
[00:53:53] Ken: Incredible stuff. I will leave your LinkedIn in the description below your name is also going to be right. Kind of under your face, the whole interview so people can check it out there. Naveed, thank you so much for coming on. I really enjoyed this conversation. You know, it looks like we did everything right on time.
[00:54:10] Naveed: Yeah. Yeah. Thank you so much, Ken, it was a pleasure being with you, and having this conversation with you. Thanks a lot!