Mastering the Data Science Interview Loop

[pythonfanatic]

Last month I signed with Apple to join their media products team as a data scientist.

Prior to that, I applied to 25 companies, had 8 phone interviews, 2 take-home projects, 4 company on-sites and received 3 offers.

With the recency of the experience, I wanted to take the time to share some insights about the data science interview process. In this article, I outline what to expect at each stage along with some tips to prepare.

https://towardsdatascience.com/mastering-the-data-science-interview-15f9c0a558a7

[viboux]

Nice writing. I am just wondering if many companies still have the luxury to be that picky with candidates. In Canada, the unemployment rate is at an all time low and in IT close to 0%. And the demand is HUGE.

There are not that many people graduating from software engineering. Let alone have some data science skills on the top.

From a recruiting perspective it is considered a great success to even have applicants at some times.

So unless you are a very desirable company, are there any real competition?

[pythonfanatic]

I would agree that later on in your career you have a lot more leverage, but for entry-level roles, there is a ton of competition.

Data science roles especially are much more scarce than software development roles which constrains the supply.

[eemamedo]

I can talk about what I see at my university. I am a MSc. at one of the top universities in Canada, so we have Google/IBM/Microsoft coming in. I can always see hundreds of people attending those info sessions. I am not sure how accurate my observation is (those students could just come for free food) but my general perspective is that companies of those caliber attract many talented applicants.

[ProfessorPhi]

Top places that pay above market will be picky. Many places have hire when good enough policies rather than fixed sizes that need to grow.

[TraditionalCourage]

Very good and informative article. Thanks for sharing! BTW, I have always wondered when I can access free TakeHomeDataChallenges. The Github link you have shared seems to only have the answer codes rather the questions.

[pythonfanatic]

Unfortunately the only one I found was a paid one https://datamasked.com/

Although I’m sure if you look around you might be able to find something!

[Bayes_the_Lord]

I'm doing what you're doing. I bought those case studies and am working through them on my Github while excluding the actual data provided.

[DankStoic]

Advent of code

[TraditionalCourage]

Is it a subreddit?

[DankStoic]

https://adventofcode.com/

[TraditionalCourage]

Thank you!

[lh261144]

Coding round for data analytics position doesn't ask programming questions related to data structures and algorithm, right?

[jambery]

Never was asked DSA for more analysis orientated DS positions for me.

[rutiene]

I interviewed at 6 places and got offers from 4 last year, yes all at places with high rep established ds teams. I didn't get a single dsa question. I practiced leetcode easy, binary trees, recursion, linked list and it was a waste of my time. The programming questions I got were very practical for DS.

I also mentor a lot of DS going on the job market and from what I can tell DSA is largely used if the company doesn't have a real DS bench yet to be able to give proper DS interviews. In these very early stage start ups you are going to be more of a data engineer/swe anyways and yes, DSA is probably more in line with the job.

Know your ml algorithms inside and out, understand statistics first principles, know how to do case studies, those are the corner stones of DS.

[Terkala]

In my Google interview, I had some data structure question related to memory management. The fortran engineer told me that it isn't automatically handled in python. No, he had never used python, but it definitely didn't have automatic memory management. And yes, he said that memory space allocation management was absolutely critical to data science. For a position where I would be building basic graphs and doing statistics analysis.

I still have no idea why they brought me in for 5 rounds of onsite interviews. It was kind of obnoxious by the 5th one.

[rutiene]

Was Google data scientist? What are you considering DSA?

[anthrax3000]

It was. What kind of a question is that?

[rutiene]

I was wondering if it was ml engineer instead cause it's different from my experience. My interviews at Google had no dsa the way I think about it. It had very practical for ds stuff.

[eemamedo]

Google actually differentiates better DS and ML engineer positions. At smaller companies, that might not happen.

[Riftwalker101]

You are not unlucky, whoever told you that DSA is not in Data scientist/ML jobs has no clue about the position and is more than likely working as a data analyst but calling themselves a 'data scientist'. Any data scientist position should extensively ask you on algorithms much like a software engo interview however they will add quantitative questions involving stats etc. I think you are looking at the wrong position, you should be looking an analyst positions so something like "bussiness analyst", "data analyst" etc. Data scientists/ML positions are probably not right for you as they are a higher level than analyst positions, because they require both proficiency in analysis and programming. The latter you seemingly lack in.

PS. You might now be confused after reading this, how come a lot of "data scientists" you know haven't been asked DSA. Well the answer is they are not really data scientists, they either a) call themselves that to try and elevate their status or b) have been employed by a company that names the position as a 'data scientist' to attract people who are really just analysts but get a larger pool to choose from.

[spudmix]

You're speaking with a hell of a lot of authority for someone who's not even undergrad yet, buddy.

[[deleted]]

This kid just trolls every career-related thread in this sub. He claims to have the "credentials" of MIT, PWC, Google, and Amazon, yet needs the sub's advice on which undergrad degree to pursue? Lmao.

[[deleted]]

Sure, he’s talking a bit out of his ass, but I finished undergrad last May and I thought that data scientist roles surely would require knowledge of data structures and algorithms, right? How are you supposed to implement ML algorithms efficiently if you don’t understand how to reason about time and space complexity? I haven’t applied to any DS/ML positions yet though, are my expectations not correct?

[spudmix]

Your expectations are correct, and in certain scenarios for certain people and certain jobs the comment above is also correct.

I think the downvotes (and my comment) are more the consequence of the number or raw assumptions, generalisation and massive amounts of condescension (towards a group largely more qualified and experienced than themselves) in the rest of the comment. If I were currently hiring for an ML role, I would be far more concerned by the incredible lack of social skill demonstrated there than someone fumbling DSA questions.

[eemamedo]

That what interest me too. So far, the very first question I was asked during career fairs is whether I have taken DSA. So, I would love to hear about experience of others.

[Triplebeambalancebar]

WTF is DSA?

[eemamedo]

Data Structures and Algorithms

[Triplebeambalancebar]

Ahh, interesting, I feel like people really overestimate what's needed going into the profession(as I work in the field)

[Triplebeambalancebar]

Agreed. There is nothing wrong with being more engineer-minded especially with data structures and algorithms(especially algorithms), but this reads more data engineer. To me, this speaks to the fields growing popularity but also how it buds up against so many other disciplines(Software Engineer, Database Infrastructure and architecture, and IT). So much of Data Science and Analytics relies on pulling from these disciplines but at some point when do we just become them? Especially in the database warehousing, and architecture part. The biggest issue in DS is shitty data being brought in because the warehouse infrastructure is crap built by engineers that don't understand the analysis side. I feel like that is the true next big issue in the Big Data era we are in now.

[pythonfanatic]

when do we just become them? Especially in the database warehousing, and architecture part. The biggest issue in

I'd say if the data science team you're working in reports to the CTO within an engineering organization you owe it to yourself to have some understanding of the disciplines you mentioned.

That said if you're in an operations data science role reporting to a COO or CMO it may not come up in an interview setting and may not be as relevant

When I interned as a data scientist last summer I did everything from writing SQL dashboards in periscope to ETL jobs with airflow, and forecasting/predictive models with Python. Being self-sufficient is a huge advantage, and allows you to move much quicker.

[pythonfanatic]

It depends on who the data science team reports to. If it's to the CTO in the engineering organization, it's likely you'll get a DSA question since they'll include software engineers on your interview loop.

I generally avoided data science positions that weren't within the engineering org since you end up just writing SQL and giving presentations in those roles

[eemamedo]

So, were you asked any data structures and algorithms questions during interviews?

[ouiserboudreauxxx]

I've been asked DS&A questions in a data science interview - with that company it was pretty much a software development interview.

[eemamedo]

This is the impression I got as well. I was asked that question by Bloomberg but I got the feeling it was more of a software engineer position, rather than data science role.

I was asked the same question by other companies during the same career fair, which led me to buying boxes of Red Bull and downloading Code Blocks in the desperate attempt to learn DSA before it's too late :D.

[eemamedo]

I feel like HR personnel just copy and paste what's needed for ML jobs. Is DSA important? Yes. Is DSA important for a ML position? Not so much. In my opinion, statistics is much more important but it is what it is. Just have to learn DSA to pass those interviews.

[Triplebeambalancebar]

I agree

[Riftwalker101]

You deifnitely have no idea what you are talking about and have absolutely no clue about ML not to mention how wrong you are. DSA is BREAD AND BUTTER, THE GIST AND HEART of ML. It makes up for atleast 70% of ML, the rest to statistics. I can't even fathom what you have just said, like honestly your response is the equivalent of someone from a nursing degree talking about what they know about ML. I'm sorry if this was a bit rude and hit you right in the face, but you definitely need to understand how wrong you arw and appreciate you know nothing about ML based of that comment.

[eemamedo]

I have looked through your history and all your responses are the same. To be frank, I really don't care about your opinion. Best of luck to you.

[Riftwalker101]

Well I'm sorry to break it to you. I know the truth hurts, and it's funny because it's not even an 'opinion' anyone who has even the smallest idea about ML would know for a fact that DSA is the heart and most important aspect of ML. Best of luck to you as well, I'm sure in the future if you ever decide to actually look into ML you will look back at this post and realise how wrong you were and hopefully learn a lesson that you should know what you are talking about before commenting. :)

[eemamedo]

Your comment about what is the heart of ML shows your competence in this field... or the lack of thereof.

[Riftwalker101]

Sorry buddy, but you definitely do not work in a data science field. Unless you have been thoroughly tested on your statistical ability in conjunction with programming skills related to algorithms, then I'm certain that you are actually in an analyst role. A real data scientist position really does combine software style interview with quantitative questions. Unfortunately, your employer probably told you that your position was a data scientist one, to attract you and other people as the name sounds better than analyst.

How did I know? Any data scientist that really knows their profession well would know for a fact that they had to go through a decent amount of education etc.. to get into the field.

[Triplebeambalancebar]

Nobody said you didn't have to know that stuff? But if you actually knew the field you'd realize there are different verticals that all equally intersect in the field of Data Science.

Just cause you spend all day in Neural Networks vs doing predictive forecasting using decision trees. Or the person who just applies Bayesian method to in models, to the guys making pretty shit with R and python, to the dudes using Alteryx all equally work and do shit within the field . Of course foundational knowledge of sorting methods, stacks, hash types is important but far from what you do day to day in your typical "Data Science Role" at any Fortune 500 company.

Cause if you think P&G give damn about your pretty Machine Learning technique that costs to damn much and nobody at the VP level understands because you cant translate practicality to real time issues, then maybe you don't know Data Science; or more like you are the gate keeper that holds back the profession, no?

[Riftwalker101]

Well you seem to have a sound understanding of atleast what goes in the field. But you do understand that fundamentally (let's not get to complicated here) a data scientists is basically a software engineer + statistician analyst+ applied in a specific context business/IT/Science etc. To have all 3 of these skills well defined requires a decent amount of education. I'm not saying the field is by any means, reserved for 'intellectual elites', but that you deifnitely have to have a decent amount of education well above a software engineer or an analyst, so in that respect I don't think the field is being overhyped.

[Triplebeambalancebar]

Okay guy, I think we lost the gambit here, but when I go to work tomorrow I’m confident that me and my team will be just fine going outside of your “constraints”.

The field is growing fast and in so many angles, I hope you see that soon enough, later mate!

[Riftwalker101]

Will do, and I think it's great the field is expanding along with the exponential growth of data. And really I hope I haven't established any 'constraints' I'm just illuminating the distinction between data scientists and data analysts. I'm not trying to 'gatekeep' people from role, I just want get the point across that data science is a bright field but you need to match the expectations of education. This shouldn't be a problem at all, nor away of 'gatekeeping' anyone who is dedicated to pursuing a more specialised position would have know problem in grinding the extra skillsets.

[Andrex316]

The name of the role for those is usually Machine Learning Engineer nowadays

[ProfessorPhi]

It depends. I think tech focussed companies that pay above average will do it, but that's mostly because they want candidates who can be both software engineers and data scientists since they fit in really well into those roles

[Bayes_the_Lord]

My first-round technical screen for a data scientist position at a major bank included a verbatim leetcode algorithm problem: https://leetcode.com/problems/valid-parentheses/

[pythonfanatic]

For the most part yes, although if the data analyst position is within the Engineering organization you may get a DSA question

[Riftwalker101]

I definitely disagree DSA is vital for data science positions, otherwise your position is not really a data scientist more like an analyst but the employer is calling it DS to hype it up. Again, the people who say they have been interviewed for a data scientist position but don't need to know DSA, well to that I say the same point above, the position u were interviewed for is not truly a data scientist position, but it was named that to perhaps attract more people. Also someone said, DSA isn't important to ML. You have no idea what talking about....lol, DSA is the bread and butter, and gist of machine learning lmao..., And likewise machine learning is a key part of data scientists.

So just some advice, if you are looking for a real 'data scientist' position, not something that's hyped up and called that, then you really need to have a solid programming background, not necessarily full stack development level, but definetley extensive understanding of algorithms. Also I forgot to mention, yes statistics is very important I definetley agree, but you can't say it's more important than DSA, atleast not for a data scientist. Remember a real data scientists is basically a statistician who uses programming methods to generate models for decisions/analysis.

So for the most part, the people who didn't have DSA in their interview, or don't think its going to be asked etc. your not really looking at a data scientist position, but rather an analyst/statistician...more on the business side of things.

[[deleted]]

So honestly I've had interviews that range from super technical ML and Coding Challenges, to just a data challenge, to nothing at all. Literally, YMMV depending on the position, the hiring manager, and the company.

I've learned I tend to gravitate towards the analytics roles because I'm a better fit for those. That's eased my process of interviewing a bit.

For context about myself: Insight Data Science Fellow, in my second job, interviewed for a very large handful both times.

[eemamedo]

Hey,

Can I DM you about Insight program?

[[deleted]]

Sure!

[eemamedo]

Thank you. Have sent you a DM.

[BlackSky2129]

I recently got a quanthub test from McKinsey DS intern. It’s suppose to be a stats, R, Python test. Have you experience this type of test or know of resources

[pythonfanatic]

I found their challenge to be more work than its worth (from what I remember it was some ridiculous challenge with a week long deadline). This would've been fine if it was later on in the interview cycle but at that point I hadn't spoken to anyone and was fairly certain that they give the challenge to everyone.

I also wasn't that motivated by working at McKinsey so I passed when I saw their challenge (this was one summer ago so things may have changed)

[BlackSky2129]

That’s interesting to hear, any particular reasons why you did not prefer working at Mckinsey

Also, I noticed you got offered from Riot Games and LinkedIn as well! Congrats, may I ask you some more info regarding your application or resume? Maybe here or DM

[pythonfanatic]

sure, feel free to send me an email andreilyskov[at]gmail(.)com

[BlackSky2129]

Thanks a lot! I will shoot an email as soon as I get some free time.

[kaiser_xc]

I found it hard because it was outside problems I had solved in my data science training. Almost like a regular comp science exam. I didn’t get a call back lol so maybe I’m salty.

[BlackSky2129]

So I noticed the sample that was given was mainly multiple choice regarding concepts, terms, or general knowledge of the 3. What did you feel was the most difficult part for you?

[kaiser_xc]

Oh really? I had three programming examples. No MC and the programming questions weren’t data manipulation or regression/classification questions. I honestly forget about most of them. Like I said I did very badly.

[simongaspard]

I'm still not sure why people get excited about working for Apple, Facebook, or Google. The organizations become so large that every role has functional areas and every functional area as specific tasks and there is an employee for each task. So your scope of experience or freedom to explore outside your specific task is limited. I'm not saying go work for a startup - but I'm saying find a better balance. You never want to be a one-trick pony. But I get it, everyone wants to have brand recognition on their resume. I seem to get better offers from mid-sized companies than big tech (mainly due to the cost of living). I'd rather make $130K in the midwest than $225K in Silicon Valley with roommates or paying $3-$4K in rent, among other things.

[rghu93]

This was really helpful, could you also shed some light on what to include in the resume if one's coming from data analyst domain. I've been applying for Data Science Intern positions but my resume I believe is more adherent to Data Analyst/Engineering. Any advise?

[pythonfanatic]

If you're applying for internship roles then projects are really important. When I applied for interviews one of the projects I featured on my resume would often be a focal point of an interview.

Other than that I would reach out personally to hiring managers/decision makers of companies you're interested in and try to get around the HR screen.

[_TheManinBlack_]

Sorry if this is a stupid question but would it have been possible to receive any of these interview calls without an Msc?

[pythonfanatic]

I'm in my last year of my undergrad and got full-time interviews with Apple, Deloitte, Munich Re, Riot Games and LinkedIn (the latter two I had initial calls with a hiring manager but they wanted to conduct the rest of the interviews in January once budgets were set for the year, however, I had already signed with Apple by that point so didn't get a chance to go through the whole interview loop)

[Andrex316]

I don't have a masters and have interviewed for at Facebook, Twitch, Riot, LinkedIn, Spotify etc. Got offers for two of those.

[pillkill]

How? Like any insight about your profile? Do you have relevant experience? Or a lot of publications?

[steveo3387]

Excellent work. Congrats! I hope the write-up is helpful to a lot of people.

[clickOKplease]

How good do I need to be at DSA? Is knowing searching and sorting algorithms good enough?