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've found in terms of completeness in the data space. Yeah, there's cracking the case interview, and there's case in point or case interview secrets. There's cracking the PM interview. There's cracking the coding interview. We were inspired by all those books because we were like, why does this not exist? Because these interviews are tough. And like to prepare, you could read like 700 different medium articles and read like five textbooks and like look at 87 sites, or you just read some book. Like that's what these books did. So that's exactly it. Like We tried our best to encapsulate the whole interview experience into one book. 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, right? Arguably, this is the most important skill to test for since 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. Absolutely, yeah. So let me add a little bit more color, right? 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 kinds of questions because it's not just about like building the best regression model. It's about like actually solving the right business questions or like working with stakeholders to figure out like what are the questions we should be answering. So that's why a lot of these companies for these kind of more product-oriented roles, product analytics roles, will be asking these product sense questions as well as business analytics roles. So I think there's so much variety in this, but what we kind of 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, you know, should help for lots of different types of interviews, this idea of first, you got to clarify your answer and make sure your answer aligns to the product and business goal before even giving the real answer. So if there's one thing we've noticed by coaching hundreds of people into these kinds of jobs and like 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 in 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're able to understand and frame the problem. This is data frame podcast. Remember that. You've got to frame the problem. You've got to clarify the problem. It's not about just jumping in and trying to give an answer, right? So I think that's one of the big things that we talk about in the framework is like 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? So let's make it a little concrete, right? If the question is, how would you design Uber's surge pricing algorithm, right? 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 is 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 people on, you know, bring more cars out on the market. Like we want to ask these kinds of questions because it might seem obvious, but like 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, like the first two things. And I think Kevin, you're big on trade-offs. So mention that.

Yeah, absolutely. You know, just like Nick said before, like there's no sort of like perfect answer for a lot of these things. So similarly, you know, there's always, there's generally not a single metric that works, right? 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, right? So as part of Facebook groups, right, like we, you know, sort of the goal of the org was to kind of reduce bad content on and bad actors on Facebook groups. But, you know, you can just do that, but simply by like getting rid of or shutting down most groups, right? But that obviously hurts engagement, right? And so it's great to consider, you know, suites of counter metrics as well. And that's something that we see candidates not doing enough of. And then outside of that, you know, again, there's no silver bullet. So, you know, product intuition alone, or like just because the AB test is, you know, says to do so, like doesn't necessarily mean immediately that you should like build something or ship something, right. So there's like a lot of real world kind of like cost benefit analysis to think about. So, you know, in general, like trade offs is always, always super, super important. And another thing to keep in mind, like, is that, you know, these interviewers, generally speaking, now, it's not always true. But a lot of times, if they're asking about like products in their domain expertise, like they thought about a lot of these problems, like for much longer and way harder than any applicants have. Right. And so that's the whole reason why they're trying to gauge your intuition and the questions you kind of ask, you know, something that's kind of like new to you. And, you know, they're really just trying to assess like how you think. Right. And I think that's why kind of to Nick's point, like it's most important to just really kind of think critically and ask a lot of questions. And hopefully the interview should be fine.

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? 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 like clarify, align to the product and business goal, and then mention trade-offs. 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'd use this metric. I'm like, well, what happened to clarifying? Like, what is Facebook dating trying to do? What's there, you know? It's so easy to just like forget about the framework, which is why practicing makes perfect because it's so easy to just like go off on your own.

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? Yeah, absolutely. Yeah. So in addition to just talking to a lot of recruiters, hiring managers and VPs, Kevin and I have also hired some people in our own past. So we've seen it both as a practitioner and as like 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 like, hey, not just how can people interview better, but like how can people get better talent? Like 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. So 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 like 12 hours, you know, or like a two hour thing, sometimes just setting up for a two hour project takes like 45 minutes, because it's just building context around it. You know, people just don't realize that. So I think take home challenges, you need people need to be really thoughtful that 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. Another interesting thing is speed really matters, especially at smaller companies. So 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. Okay. And here's the thing, Facebook and Google, they take a long time to hire their candidates. Like I'll give you an example, Google, they have like 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? Another thing we let hiring 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 is 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. So 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 get maximized my offers. I'm going to interview with 10 people and try to get six offers and play them all off each other. 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 kind of couching like, ah, 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 tenants want to anchor like, oh, yeah, this is a pretty decent offer. Like, I don't know if it's worth shopping around. And like hiring managers can use that to their advantage. Yeah. And then the other last thing is just like selling people on what actually is very unique about your company and i think this takes a lot of self-reflection from a hiring manager to even answer like every company says oh we like to have 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 gonna tell you that straight up like 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. In 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. And guess what? Two-thirds of people are like, nah, I don't want to interview here. But one-third of the people who are crazy enough to interview, like 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. 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? 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 like really train them because they're undervalued and we want to invest in you, you know? That kind of humility and like 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 like really get good talent.

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, right? What are ways data teams can think like marketers

to attract talent? Yeah, absolutely. So I think one thing is we love companies that have good engineering blogs or data science blogs because it gives candidates something to latch on to 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 like nameless brand or company, they want to work with Joe or Bob or Sally, you know, and having these kind of 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, here, and you love solving this thing, like 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, you know, in marketing, call it CTA call to action right at the bottom of like, hey, like, 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. 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, you know, I mean, I think every company is doing something interesting or different, like, you know, because otherwise, how could it compete? Right. There's something unique about each company out there. Otherwise, it gets squashed by competition. I don't know too much econ. 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. It's like figuring out what's unique and positioning that is showing that off at every stage of the interview. 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 that seems like a contradiction. You want to make them feel individual, special, and unique, except at scale. How do you do that, right? So once you framed 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 up our scale? Or what can we add to make you feel even more unique? So that we write more personalized emails, we send you a personalized gift, we send you company swag after you interview with us, we give you you know, some we give you a free trial of the product in the beginning of the interview, so that you really get to sense like what our company offers we give you, you know, if it's AWS, let's give you some AWS credits, you know, what if they hit me up with that? I mean, AWS, Amazon, they're big, so they might not need to do that. But like, if you're a developer tool or data science tool, you can offer all those things. You can send your the CEO is a big believer, like, let's say this company does some very generic stuff. But they're a big believer in like the lean startup and the lean movement and like, hey, being like very lean and efficient. 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, right? So that's like, hey, like, this is what we believe in in our company, we don't pay that well, we're very efficient. And we're 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, you know, even if you're paying less, and you're a little bit more of a bootstrap company, and it all it cost you was two books, you know, that's like 30, 40 bucks. Easy. It's great as well. And you kind of give pointers based

on company size, how to fund these activities. You know, 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. Send me a book. Exactly. It doesn't have to be this big thing. And let's be honest, like spending an hour interviewing with a data scientist that costs the company real money. So why, 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 like hundreds to thousands of dollars of lost time wages and things like that and like focus so yeah two

other things to add there would be i guess one is if if you can demonstrate how data-driven uh the firm like the you know the culture is and just like the firm actually uses data that's super helpful the same way that you know a lot of engineers when they're looking on their job hunt they want to know, like, what would their impact tangibly be, right? So are they building like the product that customers are using? Are they more focusing on internal tooling? Like, what are they kind of actually working on? And I think the same way, you know, it's less probably spoken about in public, but, you know, a lot of firms are trying to, you know, there's a broad spectrum, for example, right, early adopters to kind of more mainstream adoption of data and its use in firms. But a lot of firms these days, you know, finance, tech, wherever, are trying to become more data driven, right. And so really being able to, you know, demonstrate that, hey, like data plays, for example, at Facebook, right, everyone knows that like A-B tests are so core, and experimentation is like so core to the company culture, right? It's definitely like a very attractive kind of selling point. And so that would be kind of like the first additional tip. And then I think the second one is also, we kind of touched upon this earlier, but just basically having like good, honest job descriptions, right? So there's that phrase, might be butchering it, but you know, it's like happiness is the delta between expectations and reality, right? So in the same way, you know, a lot of candidates, especially junior ones, you know, they might have these expectations like, oh, like, you know, I'm going to join this company and I'm going to do like, I'm going to build these ML models that will like, you know, get this much, you know, revenue uplift. And in reality, it's like, well, hey, like everyone, you know, there's a lot of reasons why that probably wouldn't happen for any company in the beginning, right? So, and they kind of come in doing some internal tooling or dashboards or something. And they kind of like, are like, oh, like, you know, this is not the role that I wanted, right? And so I think really kind of making sure that you have like, hey, this is like, you know, this is the job that you will be doing, right? And listen, like we want to, especially, you know, catering toward the audience, right? Like for younger folks, younger folks are always like, oh, like, you know, especially these days, right? Like at Penn, everyone was super, so I went to Penn, right? Everyone's super like career oriented, right? Like, oh, like what's next? And, you know, how do I kind of like climb up the ladder? The same way, like, you know, hey, just for these younger candidates, like on the job descriptions or maybe when they join your firm, like just make sure that you're willing to, 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, right? So it's also about, again, it's such a hot job market these days, right? 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, right? It's this

kind of crazy thought process, but like a little bit like, hey, what do you, after this job, what are you trying to do? And let's get you to that spot. You know, it's this kind of humility thing. Cause sometimes you're like, Oh, this is the last job you'll ever have. And that's like, just not a reality. Right. So it's a really good thing for if a company can be upfront like that. And this is, we're talking about like, 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. You know, but for a lot of companies, they have to realize like, Hey, talent comes and flow 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. 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, if 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, we'll 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 support you that. And when you do, we're going to write you a check to do that. I said, wow, you're the only company who said you're going to write me a check when I quit. Like you want me to quit in a few years. I mean, they didn't go that far to say like, I want you to quit, but like that kind of like 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 run, learn something more. But most companies will pretend like 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 like making sure people are aligned to what you're offering always just helps smooth things over.

I definitely echo that. Even at DataCamp, we especially celebrate team members who exit to become founders of their own. So I definitely see where you're coming from. Given we're talking about how different organizations can compete with major tech companies in hiring, where do you view the role of upskilling when filling out a pipeline of candidates? You know, given the fierce competition over 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? So

we think 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, 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, you know, we do think that there is a place for upskilling. So as an example, you know, we were talking about, hey, maybe you have a younger candidate or a younger candidate who's like just very hungry to learn a lot and just have more and more impact, right? So we always recommend, again, in general, that hiring managers just try to learn and kind of try to like map out, hey, what does this person want out of their career? You know, what have they been learning? And what do they want to be learning? Right? And if you can kind of like, make that mental connection that like, hey, this person is, you know, really smart, really hungry, just like wants to learn a lot. You, you know, we think that it's worth kind of, you know, giving them

a shot at that. in startup land they call this like 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 like people and data will intuitively know hiring managers intuitively know and then you're faced with six resumes and then you just pick the most like risk averse choice, you know, 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, you know, people intuitively know this, and then they forget about it when faced with a reality. And I think so much of this is just like, having that humility to be like, Hey, unless you're giving that unicorn salary, you're gonna upskilling is very much a real thing. And I think that's like, 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. Like there was a lot of late nights, grinding, learning, coding, it's a very individual way to learn. Like, like, ultimately, you know, most people don't learn by watching someone code. It's by coding themselves. Right. So it's just sort of like, if that's our field, can we, can we as hiring managers really embody that and have the courage to like, when faced with these resumes, pick that, you know, it's just like a courage thing. And I know it's like not easy. And then, you know, we say all this, and then you just look at five resumes and you just pick whichever is risk averse. But I think it's just ultimately having that humility to realize like, hey, I'm sort of self taught, or I'm sort of from a diverse background. So why shouldn't I should give this person a chance or like, you know, so I think the market pushes people anywhere, anyways, that way, towards being realistic. But I think just like, if you can just right from the get go, be realistic and like be making more, 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.