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Ace the Data Science Interview

In this episode of DataFramed, we speak to Nick Singh and Kevin Huo on their latest book Ace the Data Science Interview.

Jan 2022

Photo of Kevin Huo
Kevin Huo

Kevin Huo is currently a Data Scientist at a Hedge Fund, and previously was a Data Scientist at Facebook working on Facebook Groups. He holds a degree in Computer Science from the University of Pennsylvania and a degree in business from Wharton. In college he interned on Wall Street, at Facebook and Bloomberg. He’s also the author of the DataCamp course on Predicting Click Through Rate with Machine Learning in Python.

Photo of Nick Singh
Nick Singh

Nick Singh started his career as a Software Engineer on Facebooks' Growth Team, and most recently, worked at SafeGraph, a location analytics startup. He graduated from the University of Virginia with a degree in Systems Engineering, and a minor in Computer Science and Applied Math. In college, he interned at Microsoft and on the Data Infrastructure team at Google's Nest Labs. He is the co-author of Ace the Data Science Interview.

Photo of Adel Nehme
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Takeaways


Be mindful of the 10 second rule: When applying for data science jobs, it's important that you stand out. Recruiters on average have 10 seconds to read your resume, so it's important to keep it simple and pack the most important information there.


Bring your passion: Creating a portfolio of work and being passionate about the projects you worked on is the best way to bring your best self to an interview. If you choose portfolio projects you are passionate about, it will show in the interview.


Hiring managers test for intuition, not correct answers: When being propped around data science problems in a job interview, it's not important to get the answer right. What's important is that you demonstrate sound reasoning and walk them through your problem solving—this is why good intuition always trumps correct answers.

Key Quotes

Here's something really interesting. It's called The Halo Effect — which is, traditionally, if someone is an attractive person, we think of them as nicer, smarter — all kinds of positive attributes get attributed if you're a good looking person. Now, something similar actually happens when you're passionate in a job interview. If you're passionate about your own project, suddenly you come across not as passionate about just that, but about data science, about the role, about the company and as a person. People just want to be around you. They want to work with you, when you're passionate.

A lot of times, hiring managers are asking questions about problems they thought about for much longer, way harder than any applicants have. So that’s why it’s so important to prioritize having good intuition over finding the correct answer — Kevin Huo

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Adel Nehme: Hello, everyone. This is Adel, data science educator and evangelist at DataCamp. I'm excited to kick off our first episode of 2022, not only with a fresh look and feel, but with a discussion with Nick Singh and Kevin Huo on their latest book on acing the data science interview. As we enter the new year, it seems like we're telescoping into the future of work, companies embracing remote work, the great resignation putting pressure on teams to create more fulfilling roles signals an expanding opportunity for applicants to find their dream roles in data science, but also for hiring managers to create awesome candidate experiences.

Adel Nehme: And there are no better people than Nick and Kevin to discuss these exact topics. Nick Singh started his career as a software engineer on Facebook's growth team, and most recently worked at SafeGraph, a location analytics startup. He graduated from the University of Virginia with a degree in systems engineering and a minor in computer science and applied math. In college, he interned at Microsoft and on the data infrastructure team at Google's Nest labs.

Adel Nehme: Kevin Huo is currently a data scientist at a hedge fund, and previously was a data scientist at Facebook working on Facebook groups. He holds a degree in computer science from the University of Pennsylvania and a degree in business from Warden. In college, he interned at Wall Street, at Facebook, and Bloomberg. He's also a DataCamp instructor. Now, let's dive right in.

Adel Nehme: Nick, Kevin. It's great to have you on the ... See more

show. I'm excited to unpack your latest book on acing the data science interview, best practices for applicants and hiring managers, and all of that fun stuff. But before we get started, can you tell me a bit more about your background and how you got into data science?

Kevin Huo: Sure. Glad to be on the show. Actually, Nick and I met in high school. We studied biology together and even ran a club. So it's a little bit of a somewhat unorthodox path. For me personally, I actually switched into computer science upon going college and ended up learning some stats in finance as well. So I was trying to learn as much as possible about different fields, and switched from software engineering to data science, ended up working at Facebook doing data science, and then also a hedge fund as well. And then while I was in New York, I bumped into DataCamp and I was also an instructor for a class there. So yeah, I just am pretty immersed in the space.

Nick Singh: Yeah. And my background's similar. So as I mentioned, Kevin and I, we grew up together. We were actually roommates when we both worked at Facebook, him as a data scientist, me as a software engineer on the growth team, doing a bunch of A/B tests and data driven experimentation. So not quite a data science role, but something that was very data driven as a work. And I've also held different roles in data infra at Google, and worked at an alternative data company most recently. But mostly, I'm a career coach and that's where my passion lies, helping people with careers.

Nick Singh: So Kevin and I really just bonded over the fact that like, "Why is there no cracking the coding interview for data science? Where's the leak code for data science? Why is this field so confusing?" So that's what we worked on for the last year. We combined Kevin's experience being a data scientist at Facebook and Wall Street with my own experience being a software engineer turned career coach with stints in different types of data roles as well and data companies to really come together and make this book.

What should go first on a resume?

Adel Nehme: That's great. And this book was definitely a decade in the making. I found the book to be a great resource covering the entire gamut of the interview process and preparation for a data science role. I don't want to spoil the book, but the book is broadly divided into two large sections. One on really the behavioral side of acing the data science interview, as in how should aspiring data scientists approach the job hunt, and the other one on the technical side of acing a data science interview. I want to unpack the first section now, but before, I'd love if we can settle this once and for all, what should go first on a resume, work experience or education?

Nick Singh: Yeah, that's a great question. And like all these kind of questions, man, the answer is, it depends. I know that's so unsatisfying, but here's something to really think about that will answer a lot of your resume questions. We've got 10 seconds to read your resume. That's how long a recruiter hiring manager, I imagine, is looking at your thing for. And we read top to bottom, left to write English. What does that mean? You've got to get your most important stuff up there on the top visible, obvious, so that someone just looking at it real quick can understand, "This is Adel. Here's where he works," or the most impressive thing about you, because you are trying to impress them in 10 seconds.

Nick Singh: So what goes first, work experience for education? Well, whichever one's more impressive. If you went to school at MIT but you haven't really had work experience, let's go list MIT first, or maybe you went to a no name college like me, well, I'll list the fact that I interned at Google way above the fact where I went to college. So it really depends, but I think once you think about that principle, a lot of the other advice really starts to make sense and you can apply it to your own self.

Adel Nehme: That's really great. And I love how you approach this question by putting yourself in the shoes of the audience, which are ultimately the recruiters.

Nick Singh: And people hear this and they're like, "Yeah. Okay, great." But my resume's different, they're going to spend one minute on it. Put a timer for 10 seconds, see how fast 10 seconds goes by and realize how little can be understood or read in 10 seconds. So you really got to actually like time yourself for 10 seconds and know what 10 seconds feels like to get a good resume, because it's really easy to still know all this and then make like a two page resume that has way too much stuff.

Adel Nehme: Now, related to this in the book, you outline four resume principles to live by if you're applying for jobs in the data science space. I think one of the most difficult steps in landing a job in data science is actually getting interviewed. Personally, I've had dozens of companies reject my resume despite having a graduate degree and work experience at Amazon or at Fang, and optimizing your CV is a great way to get past that hurdle. Do you mind expanding over some of your favorite principles?

Nick Singh: Yeah, absolutely. Man, it is a tough thing out there, especially it hurts when people say, "Oh, the hiring market's so hot, it's so easy." But then also, when you're on the job hunt and you're getting rejected left and right, even if you've had Fang internships or had some really top-notch experience, it's just a grind for everybody. I've been there, we've been there. That's actually why we wrote the book, not because we are the world's best job hunters that landed our first role, but because we ourselves struggled with all of this. So that's a great question. I think our answer for like my personal favorite resume principle would be the fact that how much licensing freedom you have to make it your own and not follow the rules in an effort to meet the fact that you only are going to be looked at for 10 seconds.

Nick Singh: I know that was really complicated. Let me break that down one more time. I'm trying to say, who cares about how wide your margins are, as long as it's readable and as long as you're able to present yourself in 10 seconds, that's cool. Who cares what words you bold. Maybe you want to bold the companies you've worked at because you've worked at some name brand companies, go for it. Or maybe you've worked at some random companies, but you have had some really relevant titles like data science manager, and then director of data science. And you want to show off that you had this progression, even if that's companies no one's heard of, go bold that.

Nick Singh: Or maybe you've had some really good experience where you like saved the company 200K, go list that and bold that part. So bolding, it's up to you. Sizes, fonts, it's up to you. This is really hacky. I used to make the fact that I worked at Google that said Google bigger than the other companies, and I listed it first. It's just a subtle two point difference. So even within my own resume, I didn't have it consistently, each company was the same font size, but each company wasn't what I wanted you to ask me about. I wanted to ask you, "Have you asked me about Google?" So I made that a little bit bigger. Sure, it looks a little weird, but it gets the point across that this guy worked at Google.

Nick Singh: So this is what I mean to say, even your font sizes don't have to be consistent within a resume section. So that's my biggest tip is just like, yo, you have a lot more freedom as long as you're in pursuit of this 10-second goal. Not so you can look really cool, not so it can be really visually creative or interesting. Some people do those really colorful, well designed resumes. I'm not talking about any of that, I'm just talking about whatever it takes to get red and understood in 10 seconds.

Kevin Huo: Said another way, you can almost A/B test your resume assuming you apply to enough companies.

Nick Singh: Yeah.

Adel Nehme: It's almost always working back from the goal here. If you want to put our data science for marketing hat on as an applicant, your objective is to increase your conversion rate from application to interview, and all of these subtle techniques are geared towards this goal.

Nick Singh: Absolutely. I was on Facebook's growth team and Kevin was a data scientist on Facebook groups, day in and day out, that's exactly what we're trying to do, we're like, "Hey, we want to optimize this conversion rate. Let's make the color of this button brighter. Let's make it bigger. Let's make it bold." That's the same thing on your resume. Whatever is really good, we make those things bigger. And whatever is neutral, we get rid of it or we try to minimize. Because neutral just bogs down what's good. So that's another big thing I love to tell people about their resumes. People love to put in neutral information, like for example, in the US, I see so many people who list that they know foreign languages like Spanish or Hindi or Chinese.

Nick Singh: Okay, that's great, but which of these data science jobs needs a foreign language, nobody. And that's great that you're multilingual, trilingual, but realistically, the more stuff I have to scan past, the harder it is for me to even understand what you do. So just so crazy when I see people like list all kinds of random stuff that they think spruces up their resume and makes it look more full, when in fact it actually just detracts from what's good about them, and that neutral actually ends up hurting you. You only realize if you look at it for 10 seconds, you realize, "Crap, I can't even understand what this person's about in 10 seconds because they put so much random junk and so much neutral information."

Adel Nehme: I think one common neutral information that you find a lot of CV's is that hero text introducing an applicant that often reads highly motivated professional seeking position, etc, etc. I think that takes away a lot from a resume, right?

Nick Singh: Yeah. I'm a hardworking detail-oriented data scientist right at the top. Oh yeah. Because let's be honest, the first thing you do is where did they go to school or what company did they work at most previously and what was their title? That's what I want to know. So write like three, four-sentence objective right at the top, that's quite milquetoast. So once you think about it that way, then you realize how many different things are irrelevant and how many things just don't matter as much as what traditional advice says. So we talk a lot more about that in the book, the actual, I think like 30 or so specific tips, but once you keep these high level principles in mind, a lot of it will start to just click.

What does a good portfolio look like?

Adel Nehme: Now, obviously, outside of the CV itself, a major part of building an appealing data science profile or resume are projects and building a portfolio of projects. What do you think a good portfolio looks like? And what are the principles you recommend here for candidates to stand out?

Kevin Huo: Yeah, I think we can start actually a little bit about what bad projects look like, and then Nick can talk about what good projects look like. And he has a bunch of great personal examples. I think the clear one liner here is, you don't want to use data sets that everyone already knows about and uses and is for a very specific use case, so in school or in classes, online classes like everyone's using like the Titanic data set MNIST, these kinds of data sets are really boring, everyone knows about them., it doesn't really tell anything about you. Through our like career coaching and just talking with a lot of candidates and people at these companies, we've seen a wide range of projects, and consistently, this stands out whenever someone says, "Oh, you know what, I went on Kaggle and I did the Titanic competition and I got accuracy of this much and you see this much."

Kevin Huo: That doesn't tell us anything about you. It doesn't stand out.

Nick Singh: Yeah. So, good. Good means something that actually tells me about you, that you're interested, that you're passionate, that you actually went out of your way to build something. They didn't just assign it to you or it wasn't just something you had to do in school, you actually really cared enough. Maybe you even scraped your own data, we love to see that because it speaks to your own software engineering or like hackiness, which we love. And I know a lot of companies really like that. So you scrape your own data about something you're passionate about. It tells me, this person's an interesting person.

Nick Singh: And Adel, here's something really else interesting, it's called the halo effect, which is traditionally, if someone is an attractive person, we think of themself as nicer, smarter, all kinds of positive attributes get attributed if you're a good looking person. Now, something similar actually happens when you're passionate in a job interview. If you're passionate about your own project, suddenly you come across, not as passionate about just that, but about data science, about the role, about the company as a person, people just want to be around you, they want to work with you when you're passionate.

Nick Singh: And here's the thing, you are just passionate for your own project. That's easy to get hype about your own project. I'll give you an example. In college, I worked on this thing called I love fantasy football, this idea of betting on football players and how they do in a season and awarding points based on that. And I was like, "Why can't something like that exist for music?" And I love hip hop. I'm a huge Drake fan, Drake's number one fan. certified lover boy to the core. Yeah, it's a sickness. It's like I've got to get it treated.

Kevin Huo: He's not joking by the way, check out his Instagram.

Nick Singh: Yeah. Basically, my Instagram is basically a Drake fan account where I just pretend I'm Drake. People ask me like, "Nick, what's your next move?" And I'm like, "I'm just trying to be the Drake of data science." Have me back on for another podcast where I'll tell you about it.

Adel Nehme: You're definitely on the way.

Nick Singh: Yeah. I'm on the way. But anyway, so I made this startup around hiphop music called where I treated these rappers as like stocks that you could invest in go long or short based on how much you believe that their commercial success would be. And I would track their commercial success via Spotify. So I built this project, I grew to about 2000 monthly active users. And then when I sent cold emails, when I let people at Facebook's growth team know, they're like, "Oh wow. This person actually knows how to build things, likes data, likes experimentation, actually has built real software. We want to hire him."

Nick Singh: But here's the crazy thing, that's me talking to Facebook's growth team where I eventually worked. But I use that same story and passion at Uber's growth team and Airbnb growth team and Snap's growth team. But when I also applied to fintech companies, Adel, I told them all about the stock market and they love that aspect of like, "How did you do transactions? How did you do longs and shorts?" And when I talked to data companies, they're like, "Yo, tell me more about the algorithm of how you're pricing these rappers. Tell me more about the data sets you use, how hard it was to use the data."

Nick Singh: So I'm basically just trying to say, at the end of the day, this one project carried me through so many different companies and made me come across as super passionate. And yeah, I love my data, I love my growth engineering, but at the end of the day, I love my hip hop and that shines through and then people remember me as like, "Oh yeah, that's the guy with that website that's super cool." And for fun, I actually DJ as well. So then the personal side comes up and they're like, "Oh, I want to work with this guy. Yeah, everyone else can code, but this guy actually does something with it and we like him as a person and I can remember that guy."

Nick Singh: You interview 10 people, you'll remember that this guy made that weird startup and does DJing and all that stuff. So this is what I'm trying to say, a well crafted portfolio project that's something that's not the Titanic data set on Kaggle, something that's not just like a basic project that really showcases what your interest is or what you're about, what kinds of things you like to build. Once you do that, you talk so much more articulately about it, so much more passionately, and it just improves your whole vibe in an interview and it's something that we feel like can really set you apart during the interview process.

Adel Nehme: It's really about integrating more authenticity into your interview process, with your passion and ability to articulate the intersection of your personal interest and your ambition, right? Noah Gift was on the podcast last year and he talked about what makes a great data science portfolio. And he mentioned that it produces thought leadership around a specific topic where you can take a previously unexplored data set and produce an original insight out of it. In a lot of ways, this mirrors what you're discussing since it requires that passion.

Nick Singh: Absolutely. And Kaggle, man, they have got so many data sets. Now. I said don't use Titanic data set unless you're super passionate about the movie Titanic or something. But I'm trying to say Kaggle is fine. There's just so many cool things. I love me some Indian food. And I saw there's this whole data set of Indian food recipes, just like 10,000 Indian food recipes. I wanted to do like chicken tikka masala analytics. I had like 86 questions, and you guarantee no one has explored that data set really in depth, looking at chicken tikka and trying to like do some analysis there.

Nick Singh: But it tells you something about me and my passion for food and cooking. So I'm trying to say, you don't have to have this wacky fantasy football idea. Like on Kaggle, if you like cooking, there's cooking data sets, you like basketball, there's basketball data sets. You got everything right there. And so many questions can be asked and explored. And I think it's just up to you waiting for you to do that work.

Best Practices for Cold Emailing

Adel Nehme: That's awesome. And I should definitely scrape a data set for Lebanese food. Something we've mentioned here and that is definitely a very common challenge for different types of job applicants in the data space is actually getting your foot in the door and getting an interview. I think this is even more exacerbated when recruiters or companies simply ghost applicants despite putting in the time and effort to write up a personalized resume or cover letter. In the book, you outline cold emailing and your best practices for cold emailing recruiters to get noticed by them. Do you mind expanding into that? And can you also expand into why there is this black hole effect of online job applications where resumes aren't noticed?

Nick Singh: Absolutely. Yeah. So chapter three, all about cold emails, is my favorite chapter, because it's a very core part of what's led me and Kevin to a lot of career success that we think data scientists, machine learning engineers, data analysts just don't know about, but people in the sales world, marketing world, they know all about this. So cold email is where you write an email to somebody but you don't know. So it's like not a warm introduction. It's like just a total stranger. And it doesn't have to be an email, it could be a Twitter DM, it could be a LinkedIn DM, it could be a connection request, whatever you have.

Nick Singh: It's this idea that you can approach people and pitch them on you. And you don't have to just apply online on LinkedIn or indeed, and be one of the 300 applicants and just wait there to be filtered out by some recruiter who doesn't really know who you are or why you're a good fit. It's up to you to go pitch the hiring manager, the recruiter like, "Hey, I'm Adel, here's the work I've done. Here's a link. I'm such a good fit for this role for this reason. Let's start the interview process next week." You can send that email tomorrow. Any one of us can send that email tomorrow. We don't have to wait to get filtered to be in front of the hiring manager and pitch them on why you're a good fit.

Nick Singh: Of course, this is not a bulletproof technique, you have to be respond worthy, you have to be relevant. You can't just spray and pray and be random. And that's why you pick your portfolio project in a space that shows your own passion, but also your professional interest. As I said, I'm interested in fintech, growth engineering, and data. So I did a project that encompassed each of those things. So when I wrote to these hiring managers about growth engineering, I'm telling them about how I grew to 2000 monthly active users. And when I'm talking to fintech, I'm talking about the stock market aspect and how I'm interested in finance and like consumer finance or consumer product.

Nick Singh: The point being here that cold email lets you just really tell a story in the way and control the narrative and really get in front of people in the way that LinkedIn and Indeed just never will let you do because you're just one of 300 applicants and most of the time you get filtered out. Actually, let's be honest, you don't even get filtered out, you just don't even know what happens. Most of the time, you never even hear back. You don't even get rejected, you don't hear back. At least in an email, you can follow up once or twice. And we talk about like what you should actually say in the emails in the book, but I think it's such a good technique.

Nick Singh: And I should have mentioned one thing, my last job came from sending a cold email. I called email my way to interviews at Airbnb, at Uber. But my actual last job came from writing the CEO of the small startup I worked at telling them like, "Hey, I love your company. Here's why It fit. A few days later, we're interviewing." And then soon, I worked there for almost two years. It all came from just an email I sent at like 2:00 AM one night when I was a little bored and thought their company was cool. So I think it's definitely something a lot more people need to do to escape this black hole in the online job portal where you just never hear back.

Adel Nehme: Yeah. It's really interesting. Someone like me for example, I started off as a data scientist, but now since it's at the intersection of marketing and data science, I think only now do I realize the importance of being in someone's inbox and being able to reach them and tell them this is what I'm all about. This creates a strong connection down the line.

Nick Singh: Exactly. So that's why that's our favorite thing because now that you're doing this podcast and you're reaching out to other hosts and people, you're used to sending these cold emails, but most job seekers, especially in tech, aren't used to it. But what we let people know is even though this sounds foreign, you better believe sales people, VCs, recruiters, they're sending emails all the time randomly to people. You've been reached out by recruiters randomly, so why can't you yourself reach out to the recruiters if they reach out to you randomly? Like, so it's just something we want to normalize.

Nick Singh: And we talk in the book like the exact scripts to send and the real cold emails that I and Kevin have actually sent that have landed us interviews and jobs and what we should write, it's all there in chapter three of the book.

Technical Questions

Adel Nehme: What's really nice is that it puts the power back in the hands of the candidate. The remaining chapters of the book discuss technical questions data scientists should be able to answer in a data science interview, whether probability and statistics or coding best practices. I think those chapters are gold minds of questions that summarize the technical aspects of data science. Do you mind describing the different sections in those books? More importantly, do you think that applicants should pay special focus to one type of skill over another depending on the role they're applying for?

Kevin Huo: Yeah, sure. Again, data science is super interdisciplinary, so that's one huge thing that both Nick and I recognize coming from a more software background initially in college. And so we cover the whole range of topics, we cover probability statistics, ML, there's SQL and databases, coding, and then product sense and case studies. So really the whole gamut. There's no perfect answer for what you should pay very special focus to. I think there's two good rules of thumbs that we've come across. So one is to always look at the job description. Obviously, some companies don't know the kinds of roles that they want to hire for, that's another topic, but generally speaking, they've at least tried to figure out what kind of role they want and tried to figure out, "Hey, here are the technical skills that that person should have."

Kevin Huo: And so if you are, for example, applying to like an ML engineering role, that is going to have a very distinct and different set of skills than like a data analyst role. And obviously, it always depends on the industry that it's in as well. So broadly speaking, one is, look at job descriptions. And the second is, I think there's a rough spectrum of less technical more product oriented kinds of roles. So for example, at Facebook, I was very focused on product analytics. And so generally, those types of roles data analysts or product data scientists are going to be much more focused on product, thinking about the product, using SQL and basic Python, or R.

Kevin Huo: Versus you on the other side of the spectrum, something more like ML engineering, which is very coding and ML heavy or like quant roles, which are very quantitatively heavy. There's a spectrum that you can map out, and so that's another good rule of thumb to keep in mind.

Nick Singh: Yeah. I just want to add, just ask sometimes your recruiter or the hiring manager, you just send them email saying, "Hey, I'm curious, what does your technical screening cover?" And they'll usually be like, "Oh, we'll talk about SQL in your past projects." And then that tells you what to focus on. But I think ultimately, practice makes perfect. And that's why our book has 201 real interview questions from Wall Street and Fang and some of these unicorn startups. I think ultimately, this field has so many different types of questions that even if you've had to know what to focus on, what one company calls data science, another person might have a different idea of what data science should be at their company.

Nick Singh: So at some level, you just have to know a little bit of everything and practice a little bit of everything.

Adel Nehme: What I really enjoyed the most was how diverse the set of questions are and how much they cover the data science workflow. In another life, I wanted to be a management consultant, and there were a lot of resources on how to crack the case study interview. And this feels like the closest thing I found in terms of completeness in the data space.

Nick Singh: Yeah. There's cracking the case interview, and there's case in point or interview secrets, there's cracking the PM interview, there's cracking the coding interview. We were inspired by all those books because we were just like, "Why does this not exist? Because these interviews are tough. And to prepare, you could read like 700 different Medium articles and read like five textbooks and look at 87 sites, or you could just read some book. That's what this book did. So that's exactly it. We tried our best to encapsulate the whole interview experience into one book.

Adel Nehme: Exactly. One of the last chapters in the book, outlines questions around something called product sense, and really tries to codify the business acumen data scientists need to know and prepares interviewees around that. Arguably, this is the most important skill to test for as it's not necessarily all data scientists are value driven or have this objective function of achieving business objectives. Since this is relatively open ended, I'd love to know the process by which you outlined this chapter. And if you can summarize some of the key best practices hopeful applicants can adopt here.

Nick Singh: Absolutely. Yeah. So let me add a little bit more color. So this is chapter 10, about product sense, which companies that are hiring for product data scientists, product analysts, marketing analytics jobs, they're going to be asking these kind of questions because it's not just about like building the best regression model, it's about actually solving the right business questions or working with stakeholders to figure out, what are the questions we should be answering? So that's why a lot of these companies for these more product oriented roles, product analytics roles, will be asking these product sense questions as well as business analytics roles.

Nick Singh: So I think there's so much variety in this, but what we tried to map against was what we saw very commonly being asked at Facebook, Google, and Amazon, because that represents a lot of different types of jobs. And I think for those kind of questions in the book, we talk about some of the most frequent product sense questions that are actually asked in the interview. But what framework we talk about there that I think should help for lots of different types of interviews is this idea of, first, you got to clarify your answer and make sure your answer aligns to the product and business school before even giving the real answer.

Nick Singh: If there's one thing we've noticed by coaching hundreds of people into these kind of jobs and actually doing mock interviews, what we've noticed is, people just jump in. It's so damn annoying. We try to have them think critically, and instead, they just jump into an answer. And this is one of those types of places and the interview where it's not about the answer you give or how fast you came up with it, it's about the types of questions you ask or how well you were able to understand, frame the problem. This is DataFramed Podcast, remember that, you got to frame the problem, you got to clarify the problem. It's not about just jumping in and trying to give an answer.

Nick Singh: So I think that's one of the big things that we talk about in the framework is first clarify, what are they asking? What are the success metrics? What are we optimizing for? What's the business motivation that's motivating the problem? Let's make it a little concrete. If the question is, how would you design Uber's surge pricing algorithm? You want to clarify like, well, why are we building this? Is it to balance supply and demand? Is it that the drivers have been asking for it? Is it that riders have been asking for it? Is it that Uber's just trying to maximize their revenue and they see this as a revenue maximization opportunity? Or is customer happiness something here, people are just pissed off, "Why does it take 50 minutes to get an Uber? I wish you could bring more cars out on the market."?

Nick Singh: We want to ask these questions, because it might seem obvious, but once you work with someone it's like, "Oh wow, this is a really complicated problem." So I think clarify and aligning is my framework answer, the first two things. And I think Kevin, you're big on tradeoffs. So mention that.

Kevin Huo: Yeah, absolutely. Just like Nick said before, there's no perfect answer for a lot of these things. So similarly, there's generally not a single metric that works and you should always let the interviewer know that you're thinking about various ways to approach the problem, various metrics. For example, a simple example is there's always going to be counter metrics. So as part of Facebook groups, the goal of the org was to reduce bad content and bad actors on Facebook groups. But you can just do that simply by getting rid of or shutting down most groups. But that obviously hurts engagement.

Kevin Huo: And so it's great to consider suites of counter metrics as well, and that's something that we see candidates not doing enough of. And then outside of that, again, there's no silver bullet, so product intuition alone or just because the A/B test says to do so doesn't necessarily mean immediately that you should build something or ship something. So there's a lot of real world cost benefit analysis to think about. So in general, tradeoffs is always super, super important. And another thing to keep in mind is that these interviewers generally speaking, now, it's not always true, but a lot of times if they're asking about products in their domain expertise, they thought about a lot of these problems for much longer and way harder than any applicants have.

Kevin Huo: And so that's the whole reason why they're trying to gauge your intuition and the questions you ask something that's new to you, and they're really just trying to assess how you think. And I think that's why to Nick's point, it's most important to just really think critically and ask a lot of questions, and hopefully the interview should be fine.

Case Study Interviews

Adel Nehme: Circling back maybe here to the case study interview in management consulting, a big aspect of that case study interview is not necessarily having the right answer, but being able to clearly articulate sound thinking when solving the case. That I think is the biggest win when it comes to product sense, right?

Nick Singh: Absolutely. And that's why these frameworks are so important. And we talk about them in the book and put real questions in there with the real solutions because even if we talk about this framework from coaching so many people, we tell you all this stuff, clarify, align to the product and business goal and then mention tradeoffs. And I hit you with that problem like, "Hey, what are some success metrics you'd use for Facebook dating?" And we'll just see people just jump in, "Oh yeah, I'll use this metric." I'm like, "Well, what happened to clarifying? What is Facebook dating trying to do?"

Nick Singh: It's so easy to just forget about the framework, which is why practicing makes perfect, because it's so easy to just go off on your own.

Hiring Managers Point of View

Adel Nehme: Exactly. And I'd love to pivot here maybe to discuss the hiring manager perspective instead. Now of course, I'm sure preparing for this book meant that you've also spoken with a lot of hiring managers who've been hiring data scientists. I'd love to know, given your close work with them, what do you think are some of their best practices hiring managers need to adopt when hiring talent and what are some of the biggest pitfalls they should avoid?

Nick Singh: Yeah, absolutely. In addition to just talking to a lot of recruiters, hiring managers and VPs, Kevin and I have all also hire some people in our own past. So we've seen it both as a practitioner and as someone being interviewed and then also talking to other people. So it's something that's near and dear to our heart, because ultimately, we're thinking, hey, not just how can people interview better, but how can people get better talent? That's something top of mind for us as well. So I think one very interesting thing that there's a lot of debate around which I want to stoke again here is whether take-home challenges are a good thing or not.

Nick Singh: I just want people to realize that a lot of senior talent or in-demand talent will not do your six hour take-home challenge that you think takes six hours, but actually takes 12 hours or a like two-hour thing, sometimes just setting up for a two-hour project takes 45 minutes because it's just building context around it. People just don't realize that. So I think take-home challenges, people need to be really thoughtful about their time limit for a take-home challenge and realize that they might be doing some adverse selection where it's like, hey, the best candidates might not do the take home. So I think that's one thing that we want hiring managers to really intentionally think about.

Nick Singh: Another interesting thing is speed really matters, especially at smaller companies. In the sales and marketing world, we know that time kills deals. It's all about speed and you want to close a deal fast. Hiring is a lot like that. And here's the thing, Facebook and Google, they take a long time to hire their candidates. I'll give you an example, Google, they have committees on committees, a hiring manager committee, a compensation committee. And you know what, people might put up with it because they're Google, but there's enough talent that just like, "Hey, I don't want to wait two and a half months to hear if I have the job at Google, I'm trying to job hunt next month." Or, "I already have two or three offers in hand, why am I going to wait an extra month and a half for Google when I have two or three decent ones?"

Nick Singh: I think another thing we let hire managers know is if you're not Google, you can't take two months and be wishy-washy, you have to be decisive and communicate well because you can't hide behind, "Oh, we had a 16 person committee to decide your offer," when your company's only 16 people. So I think speed matters and use that to your advantage. And the other thing I want to bring up is the primacy effect. It's where whatever you know first we tend to like or weigh more. So it's a real, real thing that hiring managers can use to their advantage. So what happens is before you go on the job hunt as a candidate, you're thinking, "Yeah, I want to maximize my compensation. I want to maximize my offers. I'm going to interview with 10 people and try to get six offers and play them off each other."

Nick Singh: But guess what actually happens? The first company that gives you a pretty decent offer, you're like, "Oh I like this company. They like me." You lose a little steam to keep interviewing after that, because you're just couching, "Uh, do I really like this company as much as the first company? I already have one offer. I'm getting a little tired with these technical interviews." So I think there's a real advantage to being the first one to give someone an offer. And again, that's where the time plays into it because can it's what anchor like, "Oh yeah, this is a pretty decent offer." I don't know if it's worth shopping around and hiring managers can use that to their advantage.

Nick Singh: And then the other last thing is just selling people on what actually is very unique about our company. And I think this takes a lot of self-reflection from a hiring manager to even answer, every company says, "Oh, we like to work hard and have fun," or, "We like to do this or that." But I think it takes a lot of humility to be like, "Hey guys, our company is pretty chill and I'm going to tell you that straight up, this is a very good company for work life balance." And here's the other thing, if your company is intense, that's also okay, you can say that, my last job, they said, "Hey, this is a high hours role. This is a small startup with a high hours role. We expect a lot of hours."

Nick Singh: And guess what, two thirds of people are, "No, I don't want to interview here." But one third of the people who are crazy enough to interview, interviewed there. And I think people want to try to appeal to everybody. And when you think about in marketing and positioning, if you try to appeal to everybody, you appeal to nobody in particular. And in this crazy market where each company is doing something unique and standing out, you can't get away with just trying to appeal a little bit to everybody. You got to be a little bit more unique and know that. So I think it's very important for your own company and your leadership to have sound positioning on why is this company unique? And what's something special we do?

Nick Singh: Do we have really good work life balance or really bad, but give you a lot of growth opportunities? Do we pay you a lot of money or do we pay you not a lot of money? And be upfront that this is not a lot of money, but we're going to invest in people and really train them because they're undervalued and we want to value. It's that kind of humility and candor is really refreshing because it stands out against the sea of other companies that are all just doing the same thing. So that's my tips for hiring managers to really get good talent.

How to attract talent?

Adel Nehme: That's really great. And I definitely agree on the honesty aspect of it as well and letting candidates know what they're getting into. I think in our conversation so far, it's been clear that applicants need to think like marketers and they need to creatively think about how to get noticed. Similarly, there are a lot of data teams and hiring managers that need to think about ways to attract talent and compete with the fangs of the world. What are ways data teams can think like marketers to attract talent?

Nick Singh: Yeah, absolutely. I think one thing is we love companies that have good engineering blogs or data science blogs, because it gives candidates something to latch onto like, "Oh, this is the kind of work they do." And it lets your own team look good. And I think ultimately, people want to work with other people. People don't want to work at this nameless brand or company, they want to work with Joe or Bob or Sally. And having these technical blogs authored with like, hey, at the bottom like a call to action, "If you like this blog and you love thinking about transportation, come join our company and work with Joe. Joe previously worked here, here and here and he love solving this thing."

Nick Singh: Humanizing that person because essentially your engineering blog is a really great way to attract talent. So I think just putting that call to action, in marketing call it CTA, call to action right at the bottom of like, "Hey, I want to work with this author," and make it really easy like, "Hey, here's a link to the careers. If you like this guy and you like this person's blog, let's do that." So I think just putting more call to actions in your materials and actually just showcasing your own company's unique values, I think that's something big. And I think, again, going back to the marketing thing and positioning, nailing your positioning is very important.

Nick Singh: And I think it's really up to you as a hiring manager to work with your leadership team or CEO, to really understand what makes your company unique. And if nothing makes your company unique, I think every company is doing something interesting or different, because otherwise, how could it compete? There's something unique about each company out there, otherwise, it gets squashed by competition. I don't know too much ecom, that's more Kevin's avenue, but you can't just be doing what everyone else is doing. And I think it's up to you to tell that story effectively. So I think that's another thing is figuring out what's unique and showing that off at every stage of the interview.

Nick Singh: And ultimately, I just want to say this one last piece, which is, this whole hiring talent thing is about how do you make a candidate feel valued and special at scale? And I know, that seems a contradiction. You want to make them feel individual special and unique except at scale, how do you do that? So once you frame the problem like that, that gives you really good ideas for, "Hey, how do we up our scale and what technology systems or process can we do to up our scale?" Or, what can we add to make you feel even more unique so that we write more personalized emails, do we send you a personalized gift?

Nick Singh: Do we send you company swag after you interview with us, we give you a free trial in the product in the beginning of interview so that you really get to sense what our company offers. If it's AWS, let's give you some AWS credits. What if they hit me up with that? AWS, Amazon they're big. So they might not need to do that, but if you're a developer tool or a data science tool, you can offer all those things. The CEO is a big believer. Let's say this company does some very generic stuff, but they're a big believer in the lean startup and the lean movement and being very lean and efficient.

Nick Singh: If you're interviewing a candidate, why can't you just send them the book for free, The Lean Startup and send them that lean production book, The Toyota Way? So that's like, "Hey, this is what we believe in our company. We don't pay that well, we're very efficient, but we're very systematic and we do a lot with less and we believe in this lean approach and we want you to join the team. And this is how we think." Boom, you stand out even if you're paying less and you're a little bit more of a bootstrap company. And all it cost you was two books that's like 30, 40 bucks. Easy.

Adel Nehme: It's great as well and you give pointers based on company size, how to fund these activities. I've been with companies that fly you out and do all these fancy bells and whistles, but there are ways that you can compete with that even as a lean startup.

Nick Singh: Send me a book. Exactly. It doesn't have to be this big thing. And let's be honest, spending an hour interviewing with a data scientist, that costs the company real money. So why try to save some money and not send that $50 gift, $60 gift when you know the whole interview process, hours of a data scientist time to evaluate you, cost the company hundreds to thousands of dollars of lost time, wages and things like that and focus.

Kevin Huo: Two other things to add there would be, I guess one, is if you can demonstrate how data driven the firm, the culture is, and just the firm actually uses data, that's super helpful. The same way that a lot of engineers, when they're looking at their job, they want to know what would their impact tangibly be? So are they building the product that customers are using? Are they more focusing on internal tooling? What are they actually working on? And I think the same way, it's less probably spoken about in public, but a lot of firms are trying to... There's a broad spectrum, for example, early adopters to more mainstream adoption of data and its use in firms. But a lot of firms these days finance tech, wherever, are trying to become more data driven.

Kevin Huo: And so really being able to demonstrate that, for example at Facebook, everyone knows that A/B tests are so core and experimentation's so core to the company culture. It's definitely a very attractive selling point. And so that would be the first additional tip. And then I think the second one is also, we touched upon this earlier, but just basically having good, honest job descriptions. So there's that phrase, might be butchering it, but it's happiness is the delta between expectations and reality. So in the same way, a lot of candidates, especially junior ones, they might have these expectations like, "Oh, I'm going to join this company and I'm going to build these ML models that will get this much revenue uplift."

Kevin Huo: And in reality, there's a lot of reasons why that probably wouldn't happen for any company in the beginning. And they come in doing some internal tooling or dashboards or something and they are like, "Oh, this is not the role that I wanted." And so I think really making sure that you have like, "Hey, this is the job that you'll be doing." And listen, especially catering toward the audience, for younger folks, younger folks are always, especially these days... I went to Penn, everyone was super career-oriented like, "Oh, what's next? And how do I climb with the ladder?"

Kevin Huo: The same way, for these younger candidates, on the job descriptions or maybe when they join your firm, just make sure that you're willing to talk with them and just, "Hey, here's how you could have more and more impact at the firm. And where do you want to go?" So it's also about, again, it's such a hot job market these days, it's not just about, "Hey, I want to work for you." It's also about like, "Hey, how can you grow the candidate's career as well?"

Nick Singh: It's this crazy thought process, but a little bit like, "Hey, after this job, what are you trying to do? And let's get you to that spot." It's this humility thing because sometimes you're like, "Oh, this is the last job you'll ever have." And that's just not a reality. So it's a really good thing if a company can be upfront like that. And we're talking about if you can't compete with Facebook, maybe Facebook doesn't have to be like that and say, "Hey, come to Facebook, be here forever." But for a lot of companies, they have to realize like, "Hey, talent comes and goes. If we can just position why this is such a good opportunity for you right now to get where you want to go and we're aligned to that," that kind of realness.

Nick Singh: Oh man, that's so awesome. And I had that in my last job where I said, "Hey, I want to be an entrepreneur." Straight up, they asked me, what do you want to do for five years from now? I'm like, "Hey, I won't be working for you guys, I'll be running my own company." And the CEO said, "Great, we're a small startup, we're scrappy, will teach you what you need to do. And you're going to build this company right now for the next few years so that you can go do your own company and we'll support you to do that. And when you do, we're going to write you a check to do that."

Nick Singh: I said, "Wow, you're the only company who said you're going to write me a check when I quit. You want me to quit in a few years." They didn't go that far to say I want you to quit, but that candor of like, "Hey, we get it." And that's the truth. Most of these early stage startups, people join them because they want to do maybe something entrepreneurial or learn something more. But most companies will pretend that's not the goal and it's like, "Oh, this is your forever home." And talent comes and goes. So just having more honesty in all these conversations and making sure people are aligned to what you are offering, always just helps smooth things over.


Adel Nehme: I definitely echo that. Even at DataCamp, we especially celebrate team members who exit to become founders of their own. I definitely see where you're coming from. Given we were talking about how different organizations can compete with major tech companies in hiring, where do you view the role of upscaling when filling out a pipeline of candidates? Given the fierce competition of a talent from a hiring manager's perspective, do you think there is a room to hire an upskill as opposed to wait for that unicorn data scientist to join your team?

Kevin Huo: A very simple way to put it is, and there's a bit of nuance to it, but if you have unicorn salary, you can get unicorn talent. Again, it's a free market. It's supply and demand. If you want to pay for those unicorn data scientists, you'll have to meet the market where it's at. It's really simple. That being said, we do think that there is a place for upscaling. So as an example, we were talking about, maybe you have a younger candidate who's just very hungry to learn a lot and just have more and more impact. So we always recommend, again in general, that hiring managers, just try to learn and try to map out, "Hey, what does this person want out of their career? What have they been learning and what do they want to be learning?"

Kevin Huo: And if you can make that mental connection that like, "Hey, this person is a really smart, really hungry, just wants to learn a lot," we think that it's worth giving them a shot at that.

Nick Singh: Yeah. In startup land, they call this slope over intercept. It's not about where you are today or where you started, it's about how fast you're growing, the slope of your learning curve. And I think that's something that people in data will intuitively know, hiring managers intuitively know, and then you're faced with six resumes and then you just pick the most risk averse choice and then complain, "Oh, why do they want so much money? They're perfect on paper and then they're asking for double the salary."

Nick Singh: People intuitively know this, and then they forget about it when faced with a reality. And I think so much of this is just having that humility to be like, "Hey, unless you're giving that unicorn salary, upskilling is very much a real thing." And I think that's okay and it should be celebrated because listen, so many of us data scientists are self-taught or even if not, even if we have a degree, let's be honest, not all of your professors are amazing. There was a lot of late nights grinding, learning, coding. It is a very individual way to learn. Ultimately, most people don't learn by watching someone code, it's by coding themselves.

Nick Singh: So if that's our field, can we as hiring managers really embody that and have the courage to when faced with these resumes pick that. It's just a courage thing. And I know it's not easy and then we say all this and then you just look at five resumes and you just pick whichever one's risk averse, but I think it's just ultimately having that humility to realize like, "Hey, I'm self-taught or I'm from a diverse background, so why shouldn't I give this person a chance?"

Nick Singh: So I think the market pushes people anywhere anyways, that way towards being realistic, but I think if you can just write from the get-go, be realistic and be making offers more intelligently to people who display that growth potential, rather than someone who just checks all these random boxes, you're going to have a much smoother time hiring candidates.

Future of Data Science

Adel Nehme: Now, as we close out, I'd love to pivot to discuss more of the future and how you believe the data science workflow and skillset will change. What do you think are some of the major trends that will shape the role into the next few years? You saw over the past year, large language models like Codex, GPT-3, AutoML, how do you think this will impact how successful data scientist or data science applicant is perceived in general?

Kevin Huo: Yeah, that's a good question. I think the short answer is that, again, the rise of a lot of more black box models will only accentuate the need for data scientists. There's a great analog, I think back in, well, I guess a long time ago now, but the ATM was invented, and they thought tellers will go out of business and it turns out afterwards empirically, there were actually a lot more bank tellers. So in the same way, if we just look at the way technology's progressing, there's been a crazy amount of innovation in the last 20 years, people are talking nowadays about generalizable AI models and just super ML, if you will. I think that they really will be a blend.

Kevin Huo: There's a stark example also in the finance industry, where there's always this question of man versus robot, and there is, is man alone the best? Is robot alone the best? Or is man plus robot is the best? And there's obviously something for debate, but generally speaking, there are things that humans are good at that machines can't do and there are things vice versa. And one of the things that humans are great at than machines, machines are extremely amazing implementation at conducting latency, just the implementation, the actual algorithm is running. And they can run a repetitive process 10 million times a second. That's what they're best at.

Kevin Huo: But humans are good at the strategic level of thinking. The same way that let's say you were to look at Wall Street and look at the different kinds of firms, so you have fundamental finance firms where it's very human driven, they don't really rely on automation. You have on the other spectrum quant firms where it's all algorithms running and trading money. At the same time, even at these quant firms, they hire researchers for a reason. The researchers are there to tell the algorithms how to think essentially. And so I think even more and more as the tools become more advanced, it basically automates a way all of the parts that were frustrating.

Kevin Huo: So every data scientist knows there's a De-Facto Workflow, so you have to get a bunch of data from somewhere, you have to look through it, clean it up, do a bunch of exploratory feature analysis. You have to do a bunch of stuff and then run a bunch of different models, do some high parameter tuning. When that all gets packaged up and simplified, instead of running a model a day or something, you would run hundreds in a day. And then that really lets you as the human architect think about, "Hey strategically, what kind of business value should I be using?" Instead of doing all the data magic kind of work.

Kevin Huo: And so I think it seems scary, again, GPT-3 is amazing. I don't think it will displace all data science jobs, it will just make data scientists focus more on the strategic higher level decision making principles.

Adel Nehme: Completely agree that there will be no data scientists automation problem anytime soon, if anything, data scientists' workflow will be supercharged. Given the automated machine learning or AutoML is increasing and more and more, so they're out of the box solutions, that can do a good job, what do you think are going to be the hallmarks of a great data science portfolio?

Kevin Huo: Yeah. Definitely the same principles as before that Nick talked about apply, obviously tying in personal interest, being creative in your approach where you don't just want to take the simplest data set and just have GPT-3 or some other model run on it and just say, "Hey, I just ran this and there you go." I think and as we've seen actually empirically with GPT-3, there's been a lot of, for example, even startups born out of sitting on top of GPT-3. So I think in the same way, especially for those that are in the ML space, just being creative with the approach and obviously trying out different kinds of models and just being more exploratory and not saying that, "Hey, the primary value add of this project and this portfolio is the actual model running," because that's going to be all automated away.

Kevin Huo: But in more of like, "Hey, this is what I'm exploring, this is maybe the sets of models I'm running together in this particular space," or using whatever data sets, that's going to be more strategic again, higher level thinking of, how does it solve a problem rather than, "Oh, hey, I ran a model and here's what it outputted." No one cares about that anymore in the future with AutoML and all these other innovations.

Adel Nehme: Finally, Nick, Kevin, where can listeners learn more about what you're working on?

Nick Singh: Absolutely. Of course, you can check out our book, Ace the Data Science Interview on Amazon. It's the number one best seller. It's doing good. So you all should check it out. You can also follow me on LinkedIn, I have 65,000 followers and I post every single day about tech career advice and data science. So that's just Nick Singh on LinkedIn. You can also check out my own website, where I talk about cold emailing, seller negotiation advice, and other blog posts for technical people that advance in their careers. Kelvin.

Kevin Huo: Yeah. My LinkedIn is Kevin-Huo, and then I think Nick forgot to mention, if you want to see the Drake side of things, his Instagram handle.

Adel Nehme: I was about to mention that.

Nick Singh: Yeah, true. You can follow us on Instagram, we have Ace the Data Science Interview Instagram, where we post interview questions and some videos from our talks and different snippets, as well as some photos of me looking like Drake or trying to be cool. It's not very cool, but I have fun with it. So it is what it is.

Adel Nehme: Awesome. Nick, Kevin, thanks for coming on the podcast.

Nick Singh: Yeah. Thanks for having us. This was a lot of fun, Adel. I love what you guys are doing.

Kevin Huo: Thanks for having us, Adel. This is super fun.



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