Jimmy Nguyen is currently a Senior Data Scientist at LinkedIn, where he uses analytics, insights, and experimentation to guide strategic decisions for LinkedIn’s Feed.
The son of Vietnamese refugees and a Silicon Valley native, Jimmy was one of LinkedIn’s first HR to engineering transfers, transforming himself from analyst to data scientist over the course of 4.5 years through a part-time Master’s in Statistics, online courses, a plethora of side-projects, and repeatedly failing with flair and style.
Since then, he has been steadily growing his data science career, leveraging both hard and soft skills to drive business impact with data. He believes that persistence, when coupled with a good attitude, is one of the best tools to have in any pursuit of life. Find him on LinkedIn.
Summary of Key Points of the Conversation
TechGuide podcast host, Ryan Atkinson, talks to show guest Jimmy Nguyen about Jimmy’s career path and what it took to land his current position as a data scientist at LinkedIn. Here are a few key takeaways, the complete transcript follows below:
- Jimmy Nguyen is a Senior Data Scientist at LinkedIn, where he uses analytics, insights, and experimentation to guide strategic decisions for LinkedIn’s Feed.
- He transitioned from being an analyst to a data scientist over 4.5 years through a part-time master’s in statistics, online courses, and various side-projects.
- Jimmy believes that persistence, coupled with a good attitude, is one of the best tools to have in any pursuit of life.
- Jimmy’s parents, Vietnamese refugees, instilled in him the values of hard work and proactiveness, which he credits for his career success.
- He started his career as a Business Intelligence Analyst at Symantec and set a goal to become a data scientist in 2013.
- To achieve his goal, Jimmy pursued a part-time master’s in statistics while working full-time, and also took online courses to improve his skills.
- He joined LinkedIn as a Compensation Analyst in 2015, where he started applying his growing data science skills to his work, building a strong reputation within the company.
- Despite facing challenges and self-doubt, Jimmy continued to push himself, taking on additional projects and roles to further develop his skills and experience.
- In October 2017, he officially transitioned into his first data science role.
- Jimmy emphasizes the importance of using data science tools to provide insights and guide business decisions, which he finds to be the most exciting part of his job.
Here is a complete transcript of the conversation:
Ryan Atkinson: Welcome, Jimmy. Super, super excited to have you on.
Jimmy Nguyen: Thanks for having me, Ryan. I’m happy to be here and looking forward to chatting with you.
Ryan Atkinson: It’s gonna be great. You have great experiences and a spoiler alert for everyone. This is our first question cuz you work at LinkedIn, but you also have a brother that also works at LinkedIn.
Jimmy Nguyen: Yes, I do. That’s right. He’s two years younger. He’s a great brother. We, so I, I started LinkedIn a long time ago in 2015. He decided to come along on the journey, I think at the end of 2021. So it’s been a year now? Yeah, a year and then some, yeah.
Ryan Atkinson: Yeah, yeah. I’m really curious of like the family dynamic and I.
Very [00:02:00] special question, like, how proud were your parents? I
Jimmy Nguyen: Oh, well, I mean, so two questions, right? First is, the dynamic is interesting in that he, he, so he we’re both in engineering, so I guess our, our senior vice president is the same or whatever, but maybe I’m more in the data science division of a certain product and he works on an engineering division of a different product.
Right. And so I think we share some commonalities where he can give me like his perspective on a certain tool based on his work experience. That’s cool. And I can give them mine. And like, I think one of the ones that we coalesced around, one of the ones that we both use is this basically a ramping platform, if you will.
Internally. It’s a way to basically gradually release a new feature to either internal or an external set of users. Right. And so he was telling me about how he used that tool versus how I used it, and, and we realized, yeah, we use. The same tool, but our perspectives on what it can do are just very different.
Right. So that was really interesting. Um, I think in terms of parents, , I would hope that mom and dad are, are proud of us. I, I think, first of all, we lo both love our parents and probably follow our par, like listen to our parents too much a little bit. We care a little bit too much about their, their opinion and, but their parents, they love us.
I think we’re both very lucky to have been raised by them. They taught us to, to work hard, be good, right? You gotta put in the work, but you also gotta go get what you want, right? Mm-hmm. My mom always used to ask both of us, like, we would always say, but it’s their fault. They did do something. Mom was, and but she always say, you know, at the end of the day, who suffers, right?
So you wanna go fix it? Go fix it, right? And I think both of us took that to heart. Um, and both of us are pretty, I think, proactive in our careers and also in life. So, Very lucky to have them as parents. Yeah.
Ryan Atkinson: Yeah. I think it’d be so cool. Just have like your brother work in like a huge tech org organization, like right next to you, so I’m sure those Thanksgiving dinners or Christmas have some hopefully not too much work talk, but
Jimmy Nguyen: No, it’s all good.
Yeah, no we, we’ve so we my brother and I, so my parents were both refugees and they came to they’re from Vietnam boat people. Right. Escape on the [00:04:00] boat. And my brother and I were lucky to be born in the Bay Area, right. And so kind of growing up here, we had very close familial ties, or I moved outta my house, you know, three, four years ago when I got married.
Very traditional Asian thing where you live with your parents, so you get married. Right. And my brother’s not allowed to leave until he gets married, so he’s stuck. Right. But we all live within like five minutes of each other. It’s really nice. Ah, that’s amazing. So it’s lucky.
Ryan Atkinson: Yep. Very lucky. Yeah.
That’s awesome. And I want to continue talking about just like your life journey because you graduated in 2012. Yeah. You then, right outta school, you work as a business intelligent analyst at Semantic. Yeah. And then you set a goal, becoming a data scientist in February, 2013. Wow. Yeah. Take us like like what were the feelings?
Like why did you wanna become a data scientist and what was like Yeah. What was calling you to do that?
Jimmy Nguyen: So it’s actually very personal. I think, and it took me a while to come up with this sort of, I would say eloquent delivery, cuz I had to spend a lot of time thinking about why I was like working so hard to be a data scientist.
Right. I actually started when I was 19 or 20. Right. So I was, I was a declared math major and finance double major at Santa Clara University. So my sophomore, junior year I was a piss poor student. Like I didn’t study, I like, I think I got like a C in advanced calculus, sorry, professor k lesinski. But at some point, like I realized like, your parents.
My parent, and I’m gonna try not to get emotional about this. I’ve told this a million times, but I get emotional about 70% of the time. So hopefully this is, we’ll see what happens, right? But I realize that my parents risked their lives on an ocean to escape. This country because they wanted to give, um, their future generations a better chance at life.
Right. And I was one of the lucky, well, me and Kevin were one of the lucky ones to, to get that right. And I was looking at myself, man. I was like, man, what a piss poor way to pay that back. Right? And so I’m like, okay, I’m not a very good math student, but let me challenge myself, right. Let me at least make myself into something.
And, and math at the time, I think was a medium for me to do it. So I just like, I think remember fall 2010, I just sat down focused. I’m just gonna, I’m just gonna study. Day in, day out, drop tennis, drop games, drop. It’s just focus and try to overcome this thing where I really thought that I [00:06:00] couldn’t do it like internally, right?
And so I went from like a straight maybe c, b minus student to a straight A minus student. I would never could quite get those A’s, cause it was on a curve. So you, it didn’t really matter what your score was, it was your ranking respect to the other students. And they would draw, like, they would put your scores, your test scores in five columns on the board, and they would draw lines.
And if you were in the first column, you got A, and your second column you got B, you didn’t matter what your score was, it was just relative to your peers. Right? So I was always like in the bottom, the first top of the second, right? So I was an A minus guy. It’s fine. I pushed myself to the limit and I was pretty proud of that.
But I, I wanted to keep carrying that. Right. I wanted to, cuz to me it was like a way to say thank you, to pay back my parents and to for sure to honor that legacy and carry it forward. Right? And say, look, they sacrificed so much for me, right? Let me make something of myself. Right. That’s amazing.
You finish undergraduate, you go into the work in finance. I started actually as a financial analyst and then transferred into business intelligence through a rotation program. But I wanted to carry that, like, I missed, that was, that had given me so much purpose, right? Mm-hmm. , to use math as a medium, to, to honor my parents’ legacy.
Now, in hindsight, does that seem really practical? Or like, at the time it made sense, right? Yeah, it gave me drive. And so I, I wanted to keep pushing , and I felt like data science was data science. I think at the time, in my opinion, data science at a top tech company, which LinkedIn was and is, I think still one of the top tech companies for sure, um, was basically the epitome of that, right?
And so I set that as a goal. I was like, okay, I wanna work in data science. I want to get good at this role. And , that’s, that was how it started. That was my drive. And, and I think it’s, I think at the core of that was really just honoring. The legacy of, of my parents. Right.
Ryan Atkinson: I think it’s really cool that, like you went from the C students is a minus with like a real passion to like mm-hmm. Make your. Make your parents proud in a way. Yeah. Cause they risk like so much. Yeah. I’m curious, like when you did say you wanted to be a data scientist and you set this goal take us through like break, break down this goal.
Like what were some of the steps you identified to reach this goal and how did you, what were the avenues you were gonna do Yeah. You were
Jimmy Nguyen: gonna take to reach it? Yeah, so I think this was around 20, I don’t know, 13. [00:08:00] Right. And I’m like, okay. At the, so at the, there’s a lot of things. At the time we didn’t have any boot camps.
There was no concept of like data science majors. Right. No data science masters, like data science was a very new field. Like Harvard had just dubbed it as like the sexiest job was 21st century. Something like that. Yeah. To me it was just the epitome of mathematics and industry is what it was, right?
Mm-hmm. I think at the time, at least what I observed was that. Teams of data scientists will be comprised of you’re either really good at computer science or you’re really good at stats, right? Yep. And I’m, I mean, I’m not the world’s best programer. I can code, but, and back then, especially I, I, I What’s code, right?
What’s challenge? Java was coffee, right? I don’t know what it’s right. So I’m like, well, I mean, I’m not that bad at math, so let me start a part-time masters in, in statistics, which is what I did in fall of 2013. Yes, interesting. Math 1 63 introduction. Probably statistics, right? And, uh, because my grades weren’t the best, I didn’t have a shot at maybe getting in some of the better schools.
So I was like, all right, you know what, we’re gonna bootstrap this, go to San Jose State, right? Get the material, teach myself, well not teach myself, but just start absorbing this mathematical knowledge so that I could advance my skills while and I did this part-time while working full-time. Right? So go out there, get the skills right.
And then simultaneously on the job trying to figure out a way, because I was in this rotation program from new college grads. Yeah. I figured out a way to get into the business intelligence division at at Symantec. And when I rotated there for my third year, that’s where I got my title changed from financial analyst to Business Intelligence Analyst.
And that’s why I also started picking up like, What I think is some core skills to part of the data science job, which is querying data with sql. Right? And in some form of visualization tool. So SQL ClickView, how do you work with it? And engineers to set up a database, right? Yeah. How do you query from it?
Visualize it? How do you, how do you deliver insights to partners? Right. That’s the stuff I was learning at work. And then at school I was slowly picking up my stats knowledge. This was 20 13, 20 14. Mm-hmm. So it’s, how’s kinda how it started? Right. The journey is still going today, 2023. But , I’ll pause there to see your thoughts.
Ryan Atkinson: Yeah, so you get this part-time master’s in like statistics you’re doing online [00:10:00] courses and whatnot. Just really getting the skills. Yeah. I’m curious cuz then you moved to LinkedIn. So when did this program, when did, when did this all finish up then
Jimmy Nguyen: So I started the masters in 2013. I also started a bunch of online courses.
I think I did like 10 online Coursera courses or something like that. Interesting. So it’s basically just teaching self-learning, right? Trying to get better. I actually didn’t finish the Master’s until 2020, so it was, yeah, so seven year part, well, it’s, it’s, it’s a two year program, but it’s 13 classes, so, and it’s a semester system, so I could really only do two per year.
I, I tried doing two or a semester, I just wasn’t able to do it right. Just too much work. Right. With a full-time job and Oh, yeah. Yeah. And so I did one class per semester for 13 semesters, which is six and a half years. Yeah, that is so cool.
Ryan Atkinson: Yeah, that is so cool. But I’m curious, cause that’s an interesting timeline because in 20, or in February, 2015, you’re at LinkedIn you’re a senior, like data analyst at LinkedIn.
Um mm-hmm. Take us like how this like job like popped up. Is this just like, so how did this happen?
Jimmy Nguyen: So if you read the fine print on the profile, I actually was hired at LinkedIn as a compensation analyst. I was not a data analyst at first. Okay. Okay. You’re a compensation analyst. I was hired a comp as a compensation analyst in February of 2015 at LinkedIn to probably one of the best managers I’ve had.
Rich, if you’re listening to this, rich, is, is. Yeah, he’s a great human being, great manager. He’s a, I think he’s a VP of some large HR function or multiple HR functions at Dropbox now. So shout out to Rich, one of the best managers I’ve ever had in my career. Um, so he hired me as a compensation analyst in, in 2015.
So at the time I. So, so in terms of the skills, right? I, my SQL was pretty good. My Tableau was decent. I mean, it was, it was okay. Like it was okay. SQL was okay. Tableau was okay. I had about three to four classes of stats and machine learning under my belt. So I, I. Start to see what I could do with this stuff.
Right. I was hired as a compensation analyst at LinkedIn. They paid me a lot more than than what Symantec did, and LinkedIn was actually one of the companies that I wanted to work for because [00:12:00] at the time I was so career focused that I thought it was the coolest thing in the world to be able to put your career out there on a professional platform and connect with other professionals.
Right. That’s sweet. The LinkedIn was also rated like one of the best places to work in like 2013, like number one or something like that. Mm-hmm. 20 13, 20 14. So I’m like, wow, this company’s reaching out to me. I take the interview, I do the interview in hr. There was maybe some Excel questions, which I had learned in my finance days, but mostly it was behavioral, right?
Mm-hmm. The role was marketed to me as an extremely analytical role in the HR function, right? I think in hindsight, I think it was true cuz LinkedIn was a very, very data driven company. And it still is, right? So I joined LinkedIn in 2015 as a compensation analyst, and I think within a month I realized, ah, shit, this may not be where I wanna be ultimately if I wanted to be a data scientist.
Right. Interesting. And, and, and because I was now within the walls of LinkedIn, I started reaching out to other data scientists and, I was, I definitely had like, oh, okay, this guy went to Stanford and he has a PhD, and what about this? Oh yeah, if you did too, right? Like, so, so there was definitely a lot of like, uh, imposter syndrome there.
And so the lucky thing was that I think Rich being the great manager that he was, and, and also realizing my passion, right? He, he, he, told me, Hey Jimmy, look, you’re hired as a competition analyst, but you can, you can make this role whatever you wanna make it, right? Mm-hmm. And of course I have to do my day-to-day things, right?
So like Excel dashboards analysis, counter offer modeling, all that stuff that the compensation analysis typically would do, right? But I think it was around that time where I started to be like, you know what? It doesn’t matter. Like if you really wanna be a data scientist, Jimmy, no one’s gonna, no one’s gonna stop you, right?
No one’s gonna, you have 24 hours in a day, right? Yeah. Do your job and then do whatever it takes to grow the skills, right? So I made friends with the HR IT team, got access to our internal databases, started querying things, calculating things like promotion, velocity, and like just all these metrics that HR didn’t have, like I would predict, like who would accept the job software using the limited stats knowledge I would have or because now you, the thing about it, you have.
Internal HR data, right? Yeah, no compensation. You have access to everything. You know how [00:14:00] much everyone’s paid, right? Yep, yep, yep. And you know how long it takes for them to get promoted. You also have access to their LinkedIn data, right? So you can say, oh, who do we hire from? How much do we pay them? If they leave our company, where do they go?
Right? So you can answer all these questions by mixing internal and external data, just for obviously very confidential internal analysis that’s shared at the aggregate level, not the individual level to leadership to help them make decisions, right? Interesting. So you start, you start doing all these crazy things that, HR hasn’t seen, right?
Like predicting who would leave the company or using clustering to, to figure out job ranges. And that’s, rich was totally on board with this. And and so I, you, you start to build a name for yourself. You know this, what the heck is this guy? Like, he’s a random analyst, hr, but he is doing all these crazy data science thing.
And I was only getting like stronger, right? I was only getting better at coding cause I was doing all these online courses. I was doing machine learning classes, right? I was taking these skills, applying it to work. It was really, really strong reputation for myself in hr. Yeah, so this was from like 2015.
In 20 15, 20 16. There, there was this talent, a analytics team that partnered with HR to do analytics. I found a way to get like a rotation with them and just take on their work just outside of, so go ahead. Yeah. No, I would say so. Sweet. Yeah, it’s awesome. And so I, and I, and again, I think Rich for me, for letting me grow that side of me and then letting me take the knowledge that I was gaining and then bring it back to his team, and then up-level the team in that regard as well.
Right. So that was, that was how I think a lot of things had to come together. One is I had to have the drive to like, not let the role define me. Right. And, you know, there was a lot of pushback too, right. It was like, who the hell is this guy coming and doing all this stuff? Like what the, what the pledge.
Right. And then there was also like lucky because I had a manager who was supportive of my growth. Right. Interesting. And so this is 20 15, 20 16. This is right on the cusp of when I was about to get my first. Oh. Also during that time I was, A lot of self-doubt, a lot of like anger, bitterness, chip on shoulder, right.
Interviewing externally, accompanies in trying to transfer internally to data science. And there was like, the first interview I tried was in like December of 2015. I was nowhere near the bar. Right. It was for a contract role. Right. So it was hourly role. I would lose all my [00:16:00] benefits, lose all my perks at LinkedIn, but I was like, screw it.
It’s in data science. I’m gonna do it. Right. Failed the interview. Cause I didn’t clear the bar anyway, but it’s fine. Right. Yeah. Then like managing that relationship with your manager too, knowing that, you know, He knows that you don’t want to be here, but he’s also trying to make it good for you and, and like being professional about it.
And yeah, it was, it was it was a challenging time, I think. Yeah.
Ryan Atkinson: Interesting. Yeah. I can tell you love, like data and numbers, because when you started talking about like putting all together, like this internal data and this external data, you, your voice like really sped up. You got really, it’s fun.
Excited. It’s fun. Yeah. It’s something you truly are passionate about. But like, did you, mm-hmm. I guess you always knew your passion about like math and statistics Of course. And it was just a seamless transition into like data
Jimmy Nguyen: science opportunity. I think. I think, I think, I think in my twenties, I’m 32. In my, I think in my twenties I was a lot more enamored with the, the techniques.
The models and the accuracy measures and precision and recall and all that stuff. Like I think there was this, it was a medium for me to build my skills. I think what excites me now, and I think maybe where, where that excitement you’re hearing right, is that using those skills you were able to get insights and guide the business with the right data at the right time to make the right decision right.
That they otherwise wouldn’t have had. That’s the fun part to me. Right. To be able to take all this and then say, Hey guys, this is what we should do. Right. And then, Have a positive impact on the business because of your recommendation. That’s, that’s the fun part. The, the tools and stuff. That’s, that’s the means, right?
And I think it’s important to know the means very well, because then what if you really know the means you can get to the right end. Right. Because you know all the nuances and the means, right?
Ryan Atkinson: Yeah. Yeah. And so October, 2017 comes around and like you get your first data science role. Right? Right. How did you officially like make this transition into like, oh, my title’s actually, like I’m a data scientist now?
Jimmy Nguyen: Yeah, so, so end of 2016, I’m gonna back up a little bit. So end of 2016, I, I did a rotation with the talent analytics team. What other members, what’s going on, on maternity leave And I stepped in and took a role right? And beginning of [00:18:00] 2017, remember around March, February, March, it was about two years working with Rich and the compensation team.
Yep. Rich sat me down. He’s like, Jimmy, I know you’re really passionate about data analytics, right? And I I really wanna make sure that you grow in that space, right? Yeah. I, I really wanna make sure that you grow. Right. Best manager ever. He introduced me to a director in data science named Shin Fu.
Okay? This is March of 2017. I remember very clearly, and I met Shin. And I told him, look, Jen I’ve been doing data science under a rock by myself for the last like two and a half, three years. No one’s given me feedback, people taking my work for their own, whatever, right? I asked them, dude, let me work for you for free on the nights and on my weekends, right?
My day, I’ll just do my job on nights and weekends. I work here for free. Right. You gimme your shittiest projects. I don’t care. Give me gimme, gimme, gimme, gimme the stuff that no one on your team wants to do, and I will do it. Right? I asked him, what do you got to lose? You don’t lose anything. Nope. I don’t, I I gain, I gain learning.
You get a free head count. The only thing I lose is sleep. I don’t give crap. Right. Because I wanna do this. Right. So he gives me a list of, his team is like 20 people list of all their, their, their, their projects. I literally go through this entire list and I break into like four categories and I’m like, all right, chin.
Here’s the crappy projects that no one wants to do, right? Yep. And he was just like, what the, what the fuck? Like who the hell is this guy? Right? Sent an email to my manager. He was like, wow, good job rich way to look after such a motivated talent. So basically I was able to finagle this like rotation thing.
And let me tell you man, that was some of the six months, the craziest, it was like, It is summer. It’s hot. Yeah. Oh, HR building. And the engineering building is a quarter of a mile away. And I bike, I’m biking back and forth between these buildings, going to data science meetings, going to HR meetings. Like I was teaching myself how to code and like Hadoop, which is like pig and hive and all these, yeah, yeah.
Big data querying languages. Right. Teaching myself json aro schema, like. [00:20:00] No, and I didn’t have a tame, I just was reading with, I, I didn’t even know how to copy stuff from like, like I could print stuff onto my like command terminal, right? Yeah, yeah. I didn’t know how to bring it into a spreadsheet. So what I would do is I would literally maximize the, the command window and copy the data on the screen and paste it, and then I would scroll down and copy it and paste it again.
Right. So I was. So during the day I was doing the HR job during night, I was doing like, so there was also things like in HR you use a pc, but in data science you use Mac and everything is set up for a Mac. So I went to IT lounge and I begged them for like their oldest like Mac laptop. They gave me some clunker.
They pulled out from the back, they all banged up and shit. They set it up as my second machine and that was the one. So I was carrying two laptops, right? Yeah. One from my HR job, one from my data science job. Yeah. So nights, weekends, that, shin was lucky to, he paired me up with two of his data scientists.
I took on some of their projects. Um, and I, I, I, I failed miserably. Like I, I, we were writing in pig script. I didn’t know how to optimize my pig script. So what I would do is I would write it. And I would wait until Friday night at 10:00 PM when the clusters would get empty, and that’s when I would run my query, because you had eight hours to execute that query.
You don’t execute eight hours, they’re gonna kick you off. Right? So I was like, okay, maximize the probability success. Run it at 10:00 PM at night. And I would open my laptop and run it and put a, like a folder on top of it. And Saturday morning, 6:00 PM 6:00 AM I’d wake up and I’d pull it and see did it finish?
And so, like, when I did that, it would, it would, it would, it would maximize my chance of success. I didn’t know how to optimize my queries. I didn’t, I’m not an engineer. Right. I, I didn’t, my mentor, she’s out there what’s up Tanya? If you’re listening to this PhD in computer science, right? Like this is like, this is her thing, right?
Yeah. My other mentor went to Harvard. Right. So , you’re up with some of the best and brightest, right. That’s the point. And so, these people are not gonna sit down and you get to teach yourself, right? Yeah. And so, you get to meet with them once every week, maybe 15, 20 minutes to check up on how you’re doing.
Right? I still remember the first like script that I wrote in Pig was so unoptimized, like Tom was so upset she took it and rewrote it herself. Um, but it’s fine. You just. [00:22:00] Just, you just give ’em the middle finger. You just tough it out. You just do it. Right. So I did that for like six months, right? And after six months I actually get pretty good at what I do.
Right? You just tough it out and you single mind only just focus on doing this thing, right? And so six months Shin sits me down and he’s like, Jimmy, you wanna interview again. And I’m like thinking, okay, this is the third time I’m trying to get to data science. Right. I don’t care. I have to try 10 times.
I’m gonna keep trying to, I get it right. That’s amazing. So, but this time around, oh man, this time I, I was ready. I was inside now. I had made friends. Yep. So I asked people, guys mock interviews. PM mock interviews, data scientist, people are willing to help you. Right? So they ask a question, they give you a sense, they won’t tell you the answers, but they’ll give you a sense of what to expect, right?
Yeah. And so I, I go onto like lead code. I do like a hundred sequel questions and then like a hundred r or Python questions and like add my PM man. I would actually, there’s a, there’s a section of the interview called product sense, right? Where they ask you a question of like, this metric is down week over week in search, how would you investigate?
Right? Interesting. Or like, what, what is how does LinkedIn member engagement on the platform lead to the success of the platform overall? Very vague, ambiguous questions where you had to put into a framework consultant, classic case study thing. Yeah. Yeah. Um, and so what I would do to practice this is that I would just go on the site and stare at it and be like, How would I make that better?
And I just type to myself like, oh, if I wanna make this search bar better, well, I think I’d probably enlarge a little bit. Oh. But if I enlarge it, it’ll take up the space underneath and the feed, and that’ll decrease revenue, which will drop revenue, but it’ll increase. Interesting. Search.
And so you, you, you have to think about the product. That’s the only way to prepare for these interviews. Right. And so, and the two days before the interview, this is getting to your October 17th, this is around September. Oh, shin set me down. It’s like, Hey, you ready to do this? And I’m like, okay.
Yeah, I’m, I’m ready. And like I, I told him, shin, I, I don’t give a rat’s ass. Okay. If you hired me as like an associate data scientist. Yeah. Right. I, I don’t, I don’t care. Right. I just wanna be your team. Like you give me a chance and you just hire me on your team and you gimme a year. And I will be the best data scientist that you have ever seen.[00:24:00]
Straight to my his face. I said it and I believed it. I believed it so much. Right. Yeah. I was on a mission like it was data science or death. Like that was, that was like, that was the drive that I, that was, it was, and like think about the relationship with my manager too in hr. He’s like, he sat me down.
He’s like, Jimmy Shin told me that you’re interviewing, okay, this is the third time you’re doing this. And he told me, I remember Richie told me, I don’t want someone on my team that doesn’t wanna be here. And that’s a very fair thing for manager to say. Yeah. Yeah. So I, I took that as, okay, there is no net this time.
If I jump and I, oh my God, right? That’s it. I’m gone. Right. But I did not care. I wanted it, so I, I just said, all right, well, well, if there’s only one in one way, then, then let’s do it. Right? And so I, I pony it up, I studied my ass off and passed the interviews, flying colors, and transferred from HR to engineering.
And, and data science. Yeah.
Ryan Atkinson: That is so amazing. I wanna give you like a round of a applause here. Is that, that’s a great, great story just in itself of like that whole transition. Yeah. I’m curious, like, like you had to feel like very confident going into these interviews. You just said six months of free work or like, were you confident?
Jimmy Nguyen: No, I, I didn’t feel confident. I felt like I was going really, like I didn’t. I knew that they were going to, I came in with the mentality that I don’t care. I, I knew they were gonna throw, I came with the mentality that you’re gonna give me something that I won’t be able to solve, but that doesn’t mean I’m just gonna stop.
I’m gonna, I’m, until that clock hits the end of the interview, I am gonna keep trying to solve it. Right. That’s amazing. And it turned out to be somewhere in between, like I was fully expecting, like the dragon to come out and bite my head off. Right. But then Dragon came out, wasn’t as strong as I thought it was.
I did de pretty decent, but. I made sure just to, I never came incompetent. I always was like, okay, what next? What next? What next? Right. Even though it was getting the answer right, I’m like, okay, what else? Right? Like it wasn’t like, you don’t stop until the clock stops. Right. So,
Ryan Atkinson: yeah. Um. One, I’m trying to, one word to just like, summarize, like this whole story I think is just like grit, pers, I’m throwing out bunch of words.
Yep. Like [00:26:00] grit per perseverance. I don’t know if I don’t, hustle’s not the right word, but it’s like an incredible, like hardworking like story. Does this all send back to like your parents being refugees, like trying to make them proud or like where does this like really come from?
Jimmy Nguyen: Pulling out the heartstrings, huh?
I see. Yeah. Uh, yes. Um, yeah. Uh, I think that’s where it comes from. Uh, it comes from, it comes from my hands. Um, it comes from trying to carry that legacy, right? It comes from honoring that legacy. It comes from the drive to honor that legacy. That’s where it comes from. Yeah. Yeah. Yeah. Yeah.
Ryan Atkinson: And do you feel like, like an, an elevated sense of like, urgency to like, do like really well in your career?
And if so, like, is that, like, is that, like, is that a good thing? Like is that sense of that’s a good thing?
Jimmy Nguyen: I think, I think back then I feel it. I felt a lot stronger back then than I did now. Right. Yeah. Because I was just so hung up on the ability that it’s part of it being in your twenties too, I think, that you just really wanted to prove yourself and Yeah, for me it was proving yourself on top of achieving this goal, on top of honoring your parents.
So I, I, I remember, I felt like it didn’t matter what happened. I was going, like, I, I didn’t care if I died. I would still be a data scientist. Like I would somehow in the after line, I was thinking like, even if they killed me, I’d still somehow with my spirit go after that job. Right? Like, that was the type of attitude I had.
Yeah. No, it’s, it’s, I think is it a good thing? It’s an effective thing. Right? But as you’ll find out if we, if we continue this journey between 2017 and 2019, There is a limit. There’s a limit. Yeah. And if you cross the limit, if you go like using an Everest analogy, if you, you live in the dead zone too long, you’re gonna die.
Right. Interest doesn’t matter how much willpower or how much strength you have. Right. There will be a point where you will burn out. Right. And I think so I think I, I learned from that journey that perseverance was a critical tool. But you also need to have time to, like, you need to know your limit.
And I think to really know your limit, you need to cross it a few times. And you need to get burned a few times before, Hey, in my opinion, you know your limit. You, you cross it, you push yourself to the absolute, human limit. You are human limit, you [00:28:00] burn out, you feel bitter, you cry, you break down.
Emotional breakdown, that stuff, right? But you don’t give up, you persist, right? You, you recover through that and you’re like, okay, I’m a little bit more seasoned now. I’m not as maybe gungho as it used to be. I’m not gonna push past the red line too long. But I do know that I can still push up to that line, maybe over that line, but just be more self-aware about whenever you’re going over that limit.
Yeah. And know that it’s not healthy to live in that zone too long and to come back down. Right. Even though internally you may feel like you want to keep calling. Right. Interesting. Have that counter voice to say, okay, push yourself. Go be beyond your limit. But at the same time, you know what, respect yourself as well.
Right. You’re a human being. Mm-hmm. There’s other things going on in your life besides just work, right? Which as you get older, you get family and like Yeah. Parents get older, you have to take care of them and more responsibility, all that stuff. Right. That stuff’s important too. Right. So yeah. I’ll, I’ll pause there.
Ryan Atkinson: I mean, what point, I mean, let’s talk about it like in 20 17, 20 18, 20 19. Like what point did you experience that and like, why did you experience that?
Jimmy Nguyen: Yeah, yeah. I think first two years in data science were tough because, these people are not, You don’t, it’s not the world’s best, and they’re not called the world’s best and brightest.
Yeah. For no freaking reason. They’re actually pretty freaking good at what they do. Right. So, I thought I, I came into data science. I was on cloud nine. I, I ch accomplished my goal. There you go. I was ready to kick ass. Right. And then I realized, yeah. I’m at the bottom of the, whatever it is.
Right. These people are good. Yeah. And I was like, okay, well I have conquered everything with hard work and extremism. Let’s just do the same thing. And I think I pushed myself too much because there was just, at the time, I just didn’t give enough my time, myself, enough time to grow. And I just was, I was just pushing so hard.
As the master’s program, right? Is. Granted, I, granted I didn’t get my master’s till 2020, so I was still like two thirds of the way through my master’s. So I would get into work at like nine, work till five, sit in traffic, go to school at six, drive back to work at seven 30 and work till 2:00 AM Right?
Like it was, it was like, like that was, I was like, alright, this is what it’s gonna take to keep up and be the best. Right. Wow. And after a while you just, [00:30:00] you, you burn, you burn out, you burn out. That’s it. There’s also a shift of like, um, sort of a EQ heavy environment to an IQ, heavy environment. Right. I think HR was very friendly, very.
I think bubbly, very kind, right? Yeah. Balanced life. You move into this world of like folks from all over the world that come, you’re like the only American on the team of very international people. Right? Interesting. These folks are here. Most likely on a visa, right? They need to stay in this country to make money, right?
They need this job to make money. So yeah, they’re super bright and they’re willing to work just as hard as you. Right? And they have more experience too, right? So I, I had this like mentality that I had to keep up, and in trying to keep up, I ultimately burned out. That was the first two years, right? So in that burnout phase, right?
What happens is I’m growing, I am learning. Yeah, right? I burn out, but there’s a certain point, I think 20 19, 20 20 maybe going a little far too early, but. I recover. And then at that point, I had been two years in data science, so I had some of the skills. You knew my drive was coming back. And so it’s like, I think about like the sum of your, in earliest stage of data science, it was a hundred percent on effort, but 0% on skill.
So a hundred plus zero is a hundred, right? Yeah. But then later on it was like maybe 80% effort, but now 80% skill, so 80 plus 80 is 160, so total effect was greater, right? But that, that you only get to 80, 80 after time because you need, you need the experience to build up your, you need the time to build up your skills.
Right. So that was something that I missed from 2017 to 2019 that I didn’t, I just didn’t give myself time to grow. Right. Interesting. Now, if you asked me would I do it again like that? Yeah, hell yeah. I’d do that again like that because a hundred percent I would do it exactly the way I did it, because then if I didn’t do it that way, I wouldn’t be able to have this conversation Right now, I wouldn’t have the self-awareness to be able to shadow other people so they don’t make that mistake.
Right. And so maybe one day when I, I wanna be a manager one day. Right. I understand that. Just because I’m successful today or just cuz I’m good today doesn’t mean that I was always good. Right? Yeah. And so with that knowledge, I can help people grow into their roles. Right? Like I try to do mentoring, right?
Ryan Atkinson: Yeah. [00:32:00] I’m curious when, so when did that shift, you said it was about two years that shift, really like a hundred percent effort to like 80% effort and 80% skill. Gradual,
Jimmy Nguyen: yeah. Yeah. It’s very gradual.
Ryan Atkinson: Yeah. Yeah. I mean, is there any way like. Is there any way someone can accelerate their skillset in a data science role?
Jimmy Nguyen: Or how, yeah, I think, I think nowadays it’s, it’s, it’s more clear cut as to how you can do it. There’s a very core set of skills that they need as a data scientist that I mean obviously sequels from a coding technical standpoint, you have to be able to query data. You have to be, be able to analyze it.
Right? So querying data, SQL is, your starting point. Yeah. Depending on the company you work at, you may have to use like Scala or Pig or Hi or whatever the heck it is. But at the end of the day, it’s the, the concept of retrieving data for analysis, right? Is something that you need to pick up. And then once you have the data, how do you analyze it, tease out the, the intricacies, all that stuff, right?
And then once you have the insights, right, how do you present it in a way that’s not just like, Hey, look what I found, but hey, this is something that you should do based on what I see in the data, right? There’s a difference between the delivering just insights and then actionable insights, right?
Mm-hmm. But that life cycle that I described for you is just like one type of data scientist, more like statistician, if you will, which is more me. Yeah. There’s a whole nother route that. It’s associated with data science, which is more on the machine learning production side, right? So if you take it from data to Python, the fork in the road is like Python, okay.
Insights or Python into production model, right? Mm-hmm. I never went the production model route, so I can’t comment as much, but I have friends that went that way as well, right? Yeah. Um, it’s, the idea is to say you take the data you put into this Python thing, and then maybe for them, they optimize it a little bit more before pushing it to some sort of function that serves like the website.
A website, right? That’s more like your machine learning engineering. But the, the core, right? If you noticed the path is the same. Retrieve, analyze, get insights or analyze, generate model. Right? But the retrieve and the analyze piece are very core to that journey. Right. I love that.
Ryan Atkinson: And I wanna talk about your journey.
We are winding down on time here a little bit more, but the past two years you go through this burnout, like, yeah, like it wasn’t the best, but you recover [00:34:00] and your drives back. All of that’s back. What has changed for you the most in your professional life? The past, like two years when it comes to LinkedIn or just how you balance work-life balance to just something like you’re proud of.
Jimmy Nguyen: Yeah, I think I’m, I’m much more, I mean, as I say, much more experienced, much more jaded, much more whatever it is. Right. It’s, yeah. You’re, you’re, first of all, objectively you’re better at what you do. Right. And it’s good, it’s good to be improving every year, try to pick up new skills. Mm-hmm. I, I think I’m a lot more humble as, not humble, but I, I just a lot more like.
Just know that yeah, you’re, you’re there, but there’s always more to be learning, right? So, and it’s okay not to know everything. Like, I’m super vulnerable about not knowing something, right? So I’m like, Hey, I don’t know, but I can always learn, I can try, right? That the idea of perseverance is still core to my being.
Right? That’s amazing. I think the, the drive I had to prove myself It’s not to prove myself, the drive is not to prove myself anymore. I think the drive now is just to be the best analytics professional you can be. Right? I don’t know why that faded. Maybe. Is this a function of getting older or Yeah.
Different priorities in life and, I have I’m married to my college sweetheart. Yeah. 11, 12 years. And we wanna have kids, right? We wanna travel. There’s other things to life other than just to work. She’s a teacher, right? And so she works very hard. And so, yeah, I think those things just together and, parents are getting older, you just wanna spend more time with them.
Right. All those things. Right. Uh, and you’d also been working for some time. It’s just a mix of, it’s just, I think it’s, it is honestly just the natural progression of life. It’s just how it is. Right. Things move. Right. So interesting.
Ryan Atkinson: Yeah. Yeah. I think cuz a lot of people that are like fresh outta school, like I am two years graduate or whatnot, like congrat, I, I, I feel like a sense of urgency to like prove myself.
Yeah, that’s okay. And like in five years I like tried to reflect on it like in five years, like, what am I gonna think? And it’s gonna be like, like right now, like, I, like I’m working very hard. Awesome. But I am curious like, what, in five years, like what’ll be like, so that’s where that question comes from.
Jimmy Nguyen: I see. Well, I mean, right. Look. Like if I, if I had to tell you like, like. Is it wrong to feel that sense of urgency and, and that drive at, at your [00:36:00] stage in life? No, absolutely not. If anything, harness it, man. Ride it. Right. Go for it. Because, no, it’s for actually everyone is different. So maybe you can keep it longer than, than I did.
I kept it for about maybe eight, nine years, right? Mm-hmm. It’s a long time. Yeah. But everyone is different and some people don’t pick up that drive until later. I have a friend who didn’t pick it up until like mid thirties and now he’s doing really great. Right. Um, so it’s. I guess the only, that’s cheesy.
The only constant has changed. But it’s true, right? Like, it’s, it’s not wrong, right? I don’t think there’s error, right or wrong. It’s just like, if it’s there be, I think just be aware of it. And then there’s a positive to every phase, right? Mm-hmm. Maybe now I, I don’t know, maybe I won’t get promoted as fast or I won’t learn as fast, right?
But I’m still learning. And, but at the same time, I’m also. Growing up the other aspects of my life. Right. That’s okay too. For you if you have that sense of urgency, take it, harness it. Drive it. Yeah.
Ryan Atkinson: That’s awesome. Yeah, I like that. And last question, so actually I usually ask the question to you, but I want to have you just ask like our audience a question, like something that they should just like consider and ask themselves and like really like.
Oh, Jimmy, ask me this question, like, what do I think?
Jimmy Nguyen: Yeah, I think the biggest thing I would ask you is I think everything you mentioned that this Audi, this podcast is targeted maybe more towards young working professionals. Right. Just remember why you do what you do and, and, and I guess know that it’s okay not to know what you want.
Right? Yeah. Like it, but the most important thing is to always view yourself at, at a point a. And always set a point B, like, I think this is what I want, right? And let’s just go for it, right? Like a lot of times folks may get stuck and say, okay, is that the right point B? What if I put all this time in and like I, I get there and it’s not what I want, right?
Then, then I, I, I’ll tell you man, like there’s a lot of things in data science that, I don’t like either, right? Yeah. I thought it was gonna be the golden whatever, right? But the point is like to always be moving, right? When once you get to that point B, whatever it is, you, you, you see what [00:38:00] you like, you see what you don’t like, right?
By the way, as you’re making that journey, you’re also changing as a person, right? Yeah. So when you get there, there’s gonna be stuff you like, and there’s gonna be stuff that you don’t like. Make that your new point. A, set a new point B and go after, right? I think life is better when you have goals, right?
When you set something, when you move towards something, right? And you are right in saying, Hey, I don’t know if that’s not that What I want. Who does know man, honestly, who, who I don’t know. Right. If tell me. Cause I don’t know. Right. But, but it just makes life, I don’t know, more meaningful, better.
Sure. Purposeful if you just have something to work towards and just always improve yourself. Right. I, I think that’s what I would leave the audience with. Like, it’s okay. Like anyone, in my opinion, anyone tells you, oh, this is what exactly I want. Maybe they’re right. Yeah. But I guarantee you, or I’m 99% sure that that’s probably not what they’ll want in 20 years.
Right. If you, if you find someone who’s that single, the mind focus 20, you know what I more than happy to prove wrong. Right. But, um, that, that’s my, my philosophy. Right. Yeah.
Ryan Atkinson: I love that. Well, Jimmy, thank you so, so much for joining us. This was such a fun episode. I’m so happy we could get you on. So thank you so, so much for being here.
It was great.
Jimmy Nguyen: Glad I could be here, Ryan. Thanks for your time. Appreciate it.