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What I Learned After Interviewing 300 Data Scientists

Season 3, episode 4 of the DataTalks.Club podcast with Oleg Novikov


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Alexey: This week we will talk about the interview process, getting hired as a data scientist — and not only data scientists. We have a special guest today — Oleg. Oleg worked as a data science manager at Uber, where he built data science teams. He also has experience building several startups in Europe. Recently he created NextRound which is a free service for practicing interviews, receiving personalized feedback, and learning materials. Welcome! (1:16)

Oleg: Glad to be here! Thank you for having me. (1:47)

Oleg’s background

Alexey: Before we go into our main topic of recruitment and interviews, let’s start with your background. Can you tell us a bit about your career journey so far? (1:50)

Oleg: I started as a software engineer. I was building websites for a few years. Then at some point I heard about the Netflix prize. It was one of the first open data science competitions, a few years before Kaggle. I got so engaged in this topic of personalization that I ended up pursuing a PhD about recommenders. Then I transitioned from an engineer to an analyst in a startup. Eventually I became a manager. I led a team of data scientists, product analytics, and engineers working on a recommender engine, and building the data infrastructure in that company. (2:00)

Standing out in recruitment process

Oleg: After that I wanted to apply for another data science job in a startup called Lyst. I thought I am probably not the most experienced data scientist applying for the job. I also needed the visa. I just began brainstorming. How do I make myself stand out, so they don’t reject me right away. I don’t even get to talk to anyone from the company. I try to reverse engineer the process and think about it. When someone applies for a job in my team, what do I do? What do I look at in CVs? What are the signals that I am trying to get? Usually when you look at someone's CV, it says “experience in Python”. It doesn’t really tell you much. It’s very subjective. Maybe the person read a blog post about this or invented the language — you never know. So, I thought “Okay, I need to stand out from the other candidates. And I also need to somehow demonstrate that these buzzwords in my CV — machine learning, big data, Python — are actually something that I am familiar with”. This was an E-commerce website and they were hiring for a data scientist to improve their recommender agent. (2:42)

Oleg: And I thought “I need to make something different”. So, instead of sending a CV, I wrote a blog post about how I would improve the recommender engine if I worked there. Since I did not work there, I did not have any data. I had to be creative. I took a list of brands that they were selling on this website, and I wrote a very simple Python script that gets a list of their followers from Twitter. Then I implemented a very simple recommender algorithm in Python. It lets you type your Twitter nickname and then you will get recommendations based on who you follow. The model was trained on these designers that are being sold by this website. So, I wrote this blog post, sent it to them. What I had in mind was “I want to show that I understand the very basics of machine learning. I can program a little bit”. Essentially, to demonstrate the very first chapter of any machine learning book. I sent it to them. Surprisingly it worked out and I got the job.

Alexey: Was it a public post? Did you just publish it on “towards data science” Or you sent a document to them? (5:13)

Oleg: I put it on medium but I put it down afterwards. I didn’t know how much of it could be public. Even though I used only data accessible everywhere, I tried to do a little bit of reverse engineering, like what kind of data is being sent from the website, what kind of APIs they are using and so on. So, I put it down. At some point there was an article about this from the other side — by the recruiter who hired me. It explained this hiring experience from there. That was very interesting to it. This is how I got that job. I focused on deployment of machine learning models, personalization ranking and a little bit of data engineering and scaling the data infrastructure. (5:19)

Oleg: After it I joined Uber in Amsterdam as a product data scientist. In a couple of years I became a manager again. I led a team of data scientists that worked on forecasting models. The goal was to predict the lifetime value of a user and quantify the impact of different events on user behavior.

NextRound — a service for free mock interviews

Oleg: Every time I applied for a job in data science, there was one thing that I really hated — when you get rejected. When you read this rejection email and it tells you nothing. You spend hours interviewing, you spend hours working on a take home assignment. And then you get some generic standard rejection email. Maybe you even get some feedback. But usually it’s completely useless and not actionable. You cannot do anything out of it. So, I began to think “What can be better? How can I make this better?” (6:38)

Oleg: I ended up building a free service where you can practice data science interviews with a chabot. Then you will receive detailed personalized feedback based on your answers with some links to relevant materials to the topics where you can improve. This is what I have been working on in the past few months. You can check this out. I have mock interviews for data scientists and product analysts. So far there have been little over 500 interviews. This is an interesting experience — just to analyze and calibrate the questions. There is never a correct answer. There is an infinite number of correct answers. If you ask some case study questions about “how would you build this kind of model?” I keep getting surprised in a positive way every day because you never know what to expect.

Why rejections are generic

Alexey: These are rejections that you got. Do you have any idea why they are genetic? I think I have some ideas, also being a hiring manager sometimes. But, do you have some ideas why these rejection letters are generic? Why can’t companies give feedback? (8:29)

Oleg: There can be many reasons. First of all, I do not know if we can call it laziness. But, let’s say, to hire one person you need to interview 20. This is realistic. This can be higher or lower. To provide detailed excellent feedback to everyone, it will take some time. Then this is really up to you whether you think it is worth it or not. I think it is worth it. Why knows, maybe these people join your company. It’s always a closed loop. There are not so many data scientists in the world or in a given city. Apart from spending time on it, another reason can be just being polite. When you try to provide some constructive actionable feedback to a person, you don’t know how they will react to this. You try to be polite and it brings you to some very standard, some generic feedback that doesn’t really pinpoint you into any certain weaknesses. There might be some legal reasons about this as well. What was your experience? What are your ideas? (8:50)

Alexey: Basically what you said. I didn’t think about being polite, I think candidates actually are looking forward to receiving feedback. I did not think about this angle, maybe I should have. think, the first one is time. Then the second is legal. You cannot always give you back because of some legal stuff. (10:11)

Starting NextRount — preparing a list of situations

Alexey: Okay, so you decided, “I hate not receiving feedback, let’s create a website where people can practice and receive feedback after?” (10:41)

Oleg: Yeah. I began thinking of my experience as a hiring manager. What can I offer? I wrote down a list of situations that kept happening from company to company, from team to team. For example, as a data scientist or an analyst, you are always balancing between working on some important model and stakeholders, asking you to work on some urgent fixes or urgent ad-hoc analysis. Or you are running an A/B test and then you see that some of the metrics improved and some of the metrics actually got worse. What do you do? I started writing down a list of situations that happened in my life that required some thinking. That was generic enough to apply to any company. Then from these situations, from these case studies, I created an interview plot. It introduces you to some context, “You are working in this company, your goal is to do this, and a stakeholder comes to you and asks you to work on this. What do you do?” (10:55)

Oleg: Depending on your answer, it will ask you different follow-up questions. Maybe switch a topic. Maybe go in depth to get more technical. I reached out to about 30 data science hiring managers to get their feedback on these interview questions that I prepared. So that it’s not completely biased by my personal experience. I got a lot of very interesting feedback from them. Then I just published this website. So far so good, everyone likes it.

Alexey: You said you had 500 interviews. The title for this event today is “what I learned after interviewing 300 data scientists”. We need to update it to 500. (13:01)

Oleg: Well... these are not interviews, but practice interviews. But then it is the same thing. (13:12)

Steps in the interview process

Alexey: Speaking of interviews. I imagine you did quite a few interviews — at least 300. How does a typical interview process look like? What are the steps in this process? (13:24)

Oleg: There is no standard here. It really depends on the company, on the size of the company. Usually, after you send your CV, a recruiter checks if your experience matches the job description. Then they might have a call with you and ask a few things about your experience that weren’t clear from your CV — to make sure that your experience is relevant to a certain role in the company. The recruiter might also ask you a few questions about the salary expectations and your availability for the new job. After that, if your experience seems relevant to this job, some companies will send you a take home assignment. Expect to spend a few hours on that. After that, there will be several rounds of interviews. One of them will be with the hiring manager — and most of the times you get to speak with the actual manager. After the interviews, there will be a debrief when the hiring manager and everyone who interviews you will discuss your experience and make the final decision. You will either get a job offer or receive this generic rejection email. (13:38)

Alexey: You mentioned the CV screening by the recruiter, home assignment and then a bunch of technical interviews. What kind of technical interviews are there? I imagine data scientists should be developers. They should be able to code. I guess one of these interviews checks coding, right? What else? (15:04)

Oleg: It really depends on the role. I even tried to visualize different types of job profiles. I took random 50 data science jobs and I found a lot of data science jobs that have literally nothing in common. It’s so generic. Data science is just an umbrella term that includes machine learning engineers and product data scientists. On one side there is an expectation that you will code a lot, you will be building and deploying machine learning models that work in production. On the other side, the expectation is that you will be running A/B tests and writing a lot of SQL — being more of a PM who is very confident working with data. (15:29)

Oleg: So, technical interviews will depend on the role. On the high level, whichever company you interview, at some point they will ask you about your previous experience. It might be a question, like “Tell us about some model that you built in the past and that you are proud of”. At some point, most likely you will be asked about some hypothetical case study. Very often, this is just a very vague and generic question like “How would you predict user journey?”

Read the job description!

Alexey: Okay. So, a product data scientist is somebody who is more an analyst, and a machine learning engineer is an engineer. And there is a whole spectrum of things. You need to look at the actual job description to figure out what exactly to expect during the process. Right? (15:56)

Oleg: That is the main advice in general. We can stop this podcast right here. The main advice is to study the job description. Learn as much as you can about the role. Try to match your experience with the role. Say, you have two years of experience: for one year you worked as an accountant, for the second year you worked as a machine learning engineer. You are applying for machine learning engineering. Will you dedicate as much time to your accounting experience as to machine learning engineering? No. If you have to put a year of your work experience or 10 years of your work experience on a piece of paper, you need to make sure it is as relevant as possible to the role you are applying for. At the end, this is all about the signal and the noise. You want to emphasize the things that are relevant to the job. Remove all the noise about your jobs that you have after college 10 years ago that are not relevant. (17:13)

CV is your landing page

Alexey: This brings us to the question about CV. This is the very first step in the job process. A recruiter or hiring manager looks at your CV — a piece of paper, one or two or three pages. They figure out if this candidate should go through the process or they reject them right away. How do we make sure that we pass this CV screen phase? You mentioned that you need to make your CV as relevant as possible to the job. For that you need to really read the job description and see how your experience matches. What else can you do there? How can you make your CV stand out? (18:28)

Oleg: I would think of CV as your landing page. Think about a website. You see an ad on the internet, you click on the link, you end up on some website that you have never seen before. In two seconds, you will decide to close it or not, if this is something relevant. You will keep looking at it. The way websites are designed and implemented has changed a lot in the past 20 years. If you compare the internet 20 years ago, next to what it is now, there are a lot of changes. There have been a lot of experiments and improvements. Think about CVs in the same way. The recruiter that is looking at your CV — what is their goal? The goal is to quickly estimate how valuable you will be in the company if you join. (19:20)

Oleg: Even more specific: their goal is to estimate what will happen if they set up an interview between you and the hiring manager. How likely the hiring manager is to offer your job or reject you? The goal of a CV for you is to get to an interview — not to get a job. It’s to make sure that the interviews will happen and you will get to talk to someone technical.

Oleg: So, when the recruiter looks at your CV, they might even not be familiar with the technical side of it. What they have is the job description. They spoke with the hiring manager about the skills required for the role. They have it on one hand, and they have your CV on the other side. Then you can think of it as of a very simple classification model that just looks for keywords. Do you have this word “TensorFlow” in your CV? If the job description says “be proactive”, are you proactive?

Oleg: We try to do this matching in your mind and understand what the role requires. Unfortunately, job descriptions are also very generic most of the time. But try to apply this job description to your past experience. Personalize your CV for every role you are applying. Your goal is to pass this screen. Then you will be able to chat about your past experience and demonstrate your technical skills.

Oleg: Recruiters are always on your side. When they look at your CV, when they have a call with you — recruiters are on your side. Their goal is to fill the role, to place someone in the role. It’s in their interest for you to get this job. If you have a call with a recruiter, ask them about the expectations, and ask them about the process. They would be very happy to help you.

Oleg: If you think about this very first step — CV screen by recruiter. If you think about it as a classifier, there is a very high penalty for false positives — if the company hires a person that is not relevant for a job. This is a big problem for both. There is a very big penalty for this kind of error. At the same time, there is zero penalty for not hiring a good person, a good candidate because. No one will even know if they were good or not. This classifier tends to create a lot of false negative errors. A lot of candidates that would have gotten on with their job get rejected.

Oleg: So, in your CV, try to first understand what is important for this role. Make sure you highlight it as much as possible and remove everything else. Remove all the noise. Whenever you are describing your past experience, be very specific about what was your personal contribution to a project. If you write on the CV, “I work on predictive models”, “Improving accuracy of predictive models”, it says nothing. You’re a data scientist, you are expected to do that. What exactly did you do? Even if you write something like “We improved the accuracy of a random forest model by X percent”. What exactly did you do? Did you identify the problem? Did you decide to use random forest? Be very specific in what was your contribution to whatever project was. This is the main advice.

Oleg: Also if you are applying to a company, to maximize your chances of pacing through this first step of the CV screening, reach out to your recruiter directly. You can try to reach out to someone from the team directly and ask them about the company. Ask them about the team in the company. If you know someone, ask for a referral. it always helps to pass this first step. At least someone will look at your CV. And probably you will secure this, this first screening call from the company.

Alexey: I think it gives an extra signal: somebody from within the company, somebody who already passed our hiring process, referred this person. So this person must be good. So, let’s take a closer look at the CV... (25:51)

Oleg: Exactly. Also, this sounds very obvious but it happens. Check for typos and errors. This is really bad — it just looks very unprofessional. Make sure the formatting is consistent throughout the document. Don’t try to lie about things, and pretend that you build something that single-handedly. This can backfire. You never know, maybe this company is interviewing your colleague. This can really backfire. Also, sometimes I see people that put self-evaluations, like Python — five stars expert. It doesn’t make a lot of sense. (26:03)

Alexey: To summarize it. CV is your landing page. On a landing page, people don’t spend more than two second. This is the time span you will get when the recruiter looks at your CV. You need to maximize the chances that you will pass this stage. You do this by highlighting what is important for the role, you remove everything else. You need to be specific about your personal contribution. It will maximize the chances that within these two seconds the recruiter will take a look at your CV and decide “okay I want to look more at this CV”. And eventually you will pass the screen. After you pass the screening, what happens next? (27:06)

Take-home assignments

Oleg: There might be a take home assignment. It will probably take a few hours for you to implement. There are controversial opinions about take-home assignments. It takes a lot of time. If you are applying to five different jobs, then you need to work on five different take-home assignments, it will take the entire week. I am not a big fan of take-home assignments for that reason: it’s more of a signal whether a person has time for them. If you’re given a take-home assignment, think about it in this way: what is the return of investment in spending your time if you get this new job? you applied for this new job for a reason — a better team, a better career opportunity, a pay increase, usually it’s at least some 15 percent. If you spend one day working on a take-home assignment, it will pay off if you get the job. Think about it from this risk/reward perspective. Because it really pays off to invest into preparing for interviews and spending your time. Emphasize your strengths and demonstrate your skills when you are interviewing. Because if you are not willing to, if you think it’s not worth time to work on this, then maybe this is not the job that you really want. (27:51)

Questions about your past experience

Oleg: After the take home assignment, there will be interviews — from three to six rounds of interviews with different potential colleagues. One of them will be with the hiring manager. On the technical rounds, you can split these into two situations. At some point you will be asked about your past experience. At some point it will be some hypothetical questions, most likely about the company. (29:32)

Oleg: If you are asked about your past experience, make sure you have a couple of stories that help you demonstrate your skills. It’s really important to practice, to know exactly what you are going to say. When you are answering these questions about your past experience, it’s not only about “I used a random forest model to predict house prices”. When you tell the story, you can also highlight a lot more than your machine learning skills. Try to emphasize your strengths between the lines. If you were the one who identified the problem and suggested to use a machine learning model to solve this problem, say it. If you suggested several modeling approaches, asked stakeholders for feedback and then defined the requirements, say that you did. If you found something interesting when you worked on a model and you decided to share it with your colleagues, and made everyone more efficient, mention it.

Oleg: It’s not just about the model. Even if it may be presented as a technical interview, even if you are asked about some model that you built in the past, by answering this question you can highlight a lot more than just your modeling skills. Those are the skills that really make the difference between a middle data scientist and senior data scientist. This really shows how autonomous you are, how proactive you are, that you can make everyone else around you more efficient and encourage others. This is really helpful.

Hypothetical case questions

Oleg: If you’re asked about some hypothetical case… It’s hard to say — there are so many companies and everyone is doing it differently. (32:03)

Oleg: You will get asked a question about some vague problem statement: “predict user churn”, “classify which users are fraudsters on our platform”, “predict how many orders or subscriptions we will have next month”. It’s always a vaguely defined problem statement. When you are answering this kind of question, the very first thing is to demonstrate that you are trying to understand the business goals. Ask why do we do it? Who will be using this model? What are the use cases? How do we define “churn”? If you are predicting which users will churn — is it people that will not place an order within two weeks? Make it more concrete, so that you understand the problem before you rush into the details. Then make sure that your narrative is very structured. You start by understanding the goals.

Oleg: Then — what kind of data do we need? What kind of model do we choose? We built the model, how do we evaluate this? Try to not jump from some very abstract things into explaining how a certain modeling approach works and then back to showing the results. It’s okay to take some time and think about, this also helps you highlight your communication skills. At each step you want to think about, “what is the goal?”. We are building the model to predict churn because of this. Then we need to get the data. What kind of data do we need? Here you can demonstrate your business, product and common sense by just thinking out loud which features will have impact on the output.

Oleg: Then you can start thinking about the modeling approach. Do we need explainability? Yes. Do we need to run it real time? No. What kind of data do we have? That’s why I choose gradient boosting. You always explain your choices. What can happen is that the interviewer will sometimes interrupt you and give you some new piece of information. They want you to go deeper on some certain topics and see if you can identify some edge cases, some limitations of the algorithm that you use, some limitations of the evaluation metric that you chose.

Oleg: At every step it’s a good practice to start with understanding the goals, explaining the goals. “What am I trying to achieve here?” And then go detailed enough so that it doesn’t sound like common sense, but you sound like an engineer. For example, when you need to explain what features you would use to build a model, don’t say “churn rate”, say something like “users that didn’t play within a certain period”. Don’t say “conversion rates”. It’s something that you can quantify, something that you can implement in SQL or in Python or whatever. It’s not just a common sense answer. You can really think of this extra step and put it in code and implement this. This is a very common reason for rejections when data scientists just start throwing a lot of different ideas. We can do this and this and that, but don’t go deep enough into any of them. At the end, you need to implement things.

Technical rounds

Alexey: So you can get from 3 to 6 rounds of different nature. Some of them will ask about your past experience. You need to prepare some stories for that, like how you did something, how you identified the requirements, how you help your colleagues, things like this. Then there are hypothetical case questions. I usually call them “case studies”. They ask you, “We want to identify users who stop using our services”. They sound vague, and this is on purpose. You need to figure out how to get more requirements. Then there could be some other types of rounds like I SQL, maybe machine learning, maybe python and things like this. (36:38)

Oleg: It really depends on the company and on the role. For some roles, you will be asked about machine learning in detail — how broad is your knowledge of different algorithms? If you say that you are familiar with one of them, how deep do you know this certain algorithm? The limitations? Can you explain how it works? In what situations it works best? And so on. If you are applying for more analytical or product data science roles, then it’s very likely that there will be some SQL questions. Most likely you will not be asked simple things like joins. Expect questions on window functions. I think it became a standard. (37:45)

Alexey: It is terrible! I have to Google this every time. People expect you to know that. (38:36)

Oleg: I think it’s okay. This is my personal perspective — I think it’s okay that if your code doesn’t work. But at least you understand what it is, how it works. You can Google it, you know, what to Google for. This is enough because you will be able to Google it at work. But at least you know that there is such a thing, that there is “rank” or that there is a “lag”. But you don’t have to remember the exact syntax. (38:42)

Handling rejections

Alexey: It often happens that I forget how to use window functions. For this company this is a super important thing and they decide to reject me. How can I handle this rejection? What happens if I get rejected? (39:10)

Oleg: First of all, don’t take it personally. It’s not an exam. it doesn’t say that you are bad. It doesn’t say that you aren’t qualified. It probably says that there was someone else who is more relevant for this certain role — and nothing else. You never know who you are competing with. Maybe there was someone with five years more of experience than you have. Or maybe someone who has more relevant experience for a certain industry. It should never harm your self-esteem. Also, realistically, interviews are far from perfect. The outcome is sometimes arbitrary. Maybe they made a mistake — this also happens. You never know. If you get rejected, ask for feedback, if you weren’t given feedback. This will hopefully help you identify what are the things that you can improve. (39:31)

Oleg: Also, depending on the company, you can just reapply to the same company in a different team right away. Usually it’s allowed. It shows that you are not a good fit for a certain role, but you are still a good fit for some different role at the same company. If you are thinking about reapplying for the same role, if it is a large enough company and it keeps hiring data scientists for a certain role, usually after some cool-off period, after a few months, you can reply. You can think of it as a learning experience. Try to get as much from it from a learning perspective: take notes, write down the questions that you were asked. It will help you become more confident in the future interviews. The feedback will help you study and prepare for the future interviews.

What to do after receiving an offer?

Alexey: Let’s say I had a couple of rejections, but I learned from them. Then, on the fifth interview, I finally got the offer. What happens then? What do I do now? Do I jump into accepting and call all my friends, saying “Hey I got this job!” Do I need to do something there? (41:36)

Oleg: We celebrate. If you get an offer, first of all, learn what you are offered. The offers can be very different depending on the country and a company. It can be just the salary, it can be salary and some equity compensation, it can also mention some bonus, it can also have some sign-on bonus. Try to learn how it compares to other companies. There is glassdoor, there are other websites where you can try to estimate and find some baselines on how relevant this offer is. (42:02)

Oleg: Then I think it’s always a good idea to negotiate because it doesn’t harm. They will not change their mind if you try to negotiate a little bit. Your success with negotiations really depends on whether you have another offer. If you have two competition offers. then it can happen that they will match the competing offer, if it’s still within their budget. If you're negotiating and it sounds like it’s not possible to negotiate on the base salary, ask if it’s possible to get a sign-on bonus. If you don’t have competing offers, then most likely you don’t have a lot of negotiation power. Maybe you will be able to get a few thousand extra, maybe not. That is why it’s good to interview in a few companies at the same time — to have this leverage.

Do recruiters pay attention to age?

Alexey: Yes. Having multiple offers is probably the best way to negotiate. We have quite a few questions. One of the questions I see in chat is from Natalia, about age. Do you know if recruiters pay attention to the age of candidates? If somebody is 40 years old, is it a bad signal for the recruiter? (43:56)

Oleg: They are not supposed to know it. Just don’t put it on your CV. Don’t put your picture, don’t put your age. Don’t say it — it’s illegal in most of the countries. (44:38)

Alexey: They are not supposed to reject you on this basis, right? (45:01)

Oleg: Of course! I think this is not a reason to not hire anyone. But if you have your date of birth or even the picture in the CV, I would just remove them. This kind of personal information on your CV is just noise. It has nothing to do with how valuable you will be for the company. Just remove it. (45:10)

Alexey: The same goes with the picture, your marital status, your address… (45:37)

Oleg: No, absolutely not. (45:45)

Getting a job with a PhD — it’s a cold start problem

Alexey: Okay, yeah. So, if this does not tell you how good you are at your job, do not put it in your CV. Probably you still need to keep the name… So, we have a question from Diksha. I’m a PhD scholar with zero industry experience, but I have knowledge required by industry experts. How do I land a good data science job? (45:46)

Oleg: Think about it as a cold start problem — when you don’t have initial data and you need to build some model. You need to create some synthetic data. You can go to Kaggle or some other places that offer you data. Ideally, you want to be creative and come up with some problem that can be solved with machine learning — or whatever is your specialization. I started by telling this story about how I got a job — by building this fake and simple recommender engine. This is exactly that. For hiring managers what matters the most is whether or not you have some experience in the data science process. Like if you are building machine learning models, then it starts with identifying the problem, goes through building the model, evaluating the model, then communicating the results, and identifying the next steps. It’s not as important whether it was your personal project or it was a commercial experience. Ideally, this should be some commercial experience when you work with the team of other data scientists, you have some supervision from your colleagues. If you don’t have it, just create this experience. Identify problems. Try to be creative. There are a lot of things to be solved with predictive models, with different kinds of models. Write a blog post about this and mention it on your CV. The best thing you can do is — try to build something for the company you are applying for. This will certainly make you stand out. (46:17)

Alexey: What you are saying is, Kaggle is great, but if you check what the company is doing. Like you did — you thought “What kind of problems they have?” And you come up with a problem, then you solve this problem, you wrote the blog post and showed it to the hiring manager. Then they were amazed and hired you. (48:04)

Oleg: Exactly. It’s not 100% that you will get hired. But if you spend time building models to have something in your CV, to be able to tell this story about your past experience, you will also learn by doing this. Don’t think “It's such a waste of time. They will not hire me. I will spend a weekend or I will spend the week working on this project and writing this blog post. And then, at the end, it will not work out. Then it was a waste of time.” No, you learned. You’re a data scientist. This is what you chose to do. This is interesting. This is exciting. And you learn some new things. You will benefit in any way, if you do it. (48:24)

Should I answer rejection emails?

Alexey: Thank you. Another question we have: Some rejection emails look quite human. The question is, is it worth answering such emails? Do the recruiters even look at what you write to them after the rejection? (49:10)

Oleg: “Thank you”. There is no point in arguing. That is probably the worst thing you can do. Just say that you appreciate the feedback. This is the best thing you can do because maybe you will apply to the same company and it will be the same recruiter. Maybe you will apply to a different company and this recruiter will change jobs as well. This happens very often. Try to not burn bridges. Even if you think it was unfair to you, try to learn from it. I think you can only appreciate and send a “Thank you” email. And thank for feedback if you found it actionable and useful. (49:31)

Negotiating when my salary is low

Alexey: Thank you. Another question we have from Muhammad is related to negotiations. How do I negotiate when my current salary is quite low? I think we covered that — by having two offers. But let’s say, I just have one offer and my current salary is low. What can I do to make sure that I try to get the best possible offer from this company? (50:17)

Oleg: Just try to put yourself in the shoes of the company. Think about them. They offer you some number. You can either accept it or not — and then they lose you. They will need to hire someone else. They are not supposed to know what your current salary is. Also in a lot of countries it’s just illegal to request this. If they ask you, you don’t have to answer. You can always just answer “The offer I am expecting from you is a good signal of how important data science is for the company”. You don’t have to tell the exact number that you are making right now. Try to make it more like “Here are my skills, you interviewed me, you made me an offer, you found me valuable for the company. But I find it low. So, I am hesitant about accepting it. What we can do about this?” (50:46)

Alexey: So, try to negotiate. When you have another offer you can always say “If you don’t do this, I will go to the other company”. But in negotiations you always have an option of not accepting an offer. So, if you don’t have another offer and you just have one, you always have an option of staying at your current place or not accepting. This is the best alternative. You can just say “For this number, it doesn’t make sense for me to change a job.” (51:53)

Oleg: If it’s true, you can say “I have a good performance, there’s a good chance that I will get promoted next year. I’m not sure if it’s worse for me to join with an offer like this. Maybe you could offer me a sign-on bonus that would offset this? This is the bonus that I am expecting in my current company”. So try to explain that you are losing something, you have a better option than the offer that they offered you. And they have to offer something else for you. (52:30)

Should I apply for jobs that require 5 years of experience?

Alexey: Thank you. Another question we have from somebody who just finished their PhD. The position they want to apply to requires 3-5 years of experience in industry. They don’t have it obviously because they just graduated from PhD. Do you think it still makes sense to apply for this position that requires some industry experience? Or is it not worth spending the time? (53:09)

Oleg: It sounds like it is some entry-level position, if it is 3 years of experience. It wouldn’t harm if you apply. It also depends on your PhD. This definitely happens when people with no commercial experience get hired at this kind of roles. As long as you believe that you could perform well in this role, If when working in your PhD you have gotten relevant experience. If you were working on something very theoretical and never implemented machine learning models, if you think your programming skills are not good enough, and it’s very clear that the role expects you to program a lot, then don’t apply. But, in general, job descriptions are flexible. Sometimes they are very standard. If you have one year of experience or less, you should definitely give it a try. (53:38)

Alexey: I liked your point, that it’s a cold start problem. You need some synthetic data to bootstrap your profile. Try to get some experience, not necessarily from the industry. If this matches the candidate profile they are looking for, maybe the hiring manager will just go for this. Even though you don’t have three years of experience, but because the project you did is so great, they will at least talk to you. (54:39)

Tricking applicant tracking systems

Alexey: Another question: I heard that these days, named entity recognition and some other things are used for screening CVs. It’s not a human that looks at the CV but a robot. You need to just to maximize your chances for applying for a job. Do you just need to copy the keywords you see on the description and paste them in your CV to maximize your chances? (55:17)

Oleg: Whatever you do, don’t put the keywords that you are not familiar with. You probably have more experience than some five keywords. Or you cannot put a year of experience or more than a year of experience in one page of paper. Try to really make it as relevant as possible and remove everything that is not relevant. Sorry, I forgot what was the question? (55:46)

Alexey: If you should just copy and paste things from the description. (56:15)

Oleg: What automatic screen, what happens often is: you submit a CV in PDF. Then it’s automatically parsed and stored in some applicant tracking system. The parsing is used to have a database with structured data. This is a data problem. You have a thousand CVs in very different formats — PDFs and images. You want to have it in some structured format in a table where you have the name, years of experience and current role. So, the CVs are parsed by applicant tracking systems. But I have never heard of some automatic tool that rejects people based on their CVs. This is a myth that for some reason became very popular. Even if it happens, this is very rare. So, in 99% cases a human being will look at your CV. (56:19)

Alexey: Unless there are 300 or 400 applicants for a position — which happens these days. Probably what recruiters do is just whoever applied first they just go through this. So they still don’t reject automatically based on some tracking system. (57:23)

Oleg: That is true for some roles: there are a lot of applicants. It’s not that you get rejected automatically because your CV was not in the right format, but because there was a hundred other people. (57:43)

Alexey: Sometimes it’s not humanly possible to look at all the applicants. That’s unfortunate. (57:57)

What else Oleg learned after interviewing 300 data scientists

Alexey: I wanted to ask you a few things. What did you actually learn after interviewing 300 data scientists? (58:14)

Oleg: A lot of things. First of all, I cannot walk into an interview with some certain expectations and answers. Sometimes the answers I hear are correct and probably are even better and more efficient than what I expected. This happens. You should never have this bias, like “This is the correct answer that I am expecting. If it’s not true, then this is wrong.” This happens all the time. This is a very humbling experience. Also, for most of the questions, no matter how you phrase it, especially if it’s some very vaguely defined case study, usually there are four, five, six different paths of answering the question. (58:24)

Oleg: There are a number of ideas like “random forests are always better than linear regression”, right? There is something like this in the air. But this is not true. In some cases, simple parametric models may perform better. Even if they require you to spend more time doing processing. Even if they have a lot of reasons to not perform well. There are cases when a parametric model is just better. Because it’s able to extrapolate and this cannot. This kind of answer I receive quite often, when you introduce some context. When it‘s clear that you need to extrapolate, when it’s very clear that the data will be beyond the range of the training data set. Also, there is a common misconception that deep learning is better than trees and trees are better than parametric models. Try to understand the problem, try to really understand the limitations of algorithms.

Oleg: Another thing I learned from 300 interviews: it would be nice to reverse the timeline. To know what feedback you would get after the interview — even before going to the interview. It sounds like a plot for some movie. But this is exactly what I’m trying to solve with NextRound. You try interviewing, you get your feedback: there are your strengths, here are your weaknesses, here are the materials to improve. Then, on the next iteration, you go to a real interview. Having this feedback and having applied this feedback and having read these materials will help improve. Because this is how modeling works. You run iterations. You tune a few parameters and expect it to be better. I think interviews itself is a really good learning experience. This is not very obvious — it’s usually perceived as an exam, an assessment.

Oleg: Another thing: it’s very important to ask questions on the interviews as a candidate. This is a two-way street. You’re also choosing. You’re deciding if you will like working in the company. And the company decides whether you will be a valuable team member. For some reason, the time is distributed for 45 minutes, it’s an exam, and then for 5 minutes at the end, if you are lucky, you will be able to ask a few questions.

Oleg: If you get these five minute, use as much of this time as possible. it’s in your interest to learn about the company. By asking questions you can also highlight your strengths. By asking questions about the company, about the workflow in the company, about the teammates you can highlight that you care about the culture. That you care about the flexibility, you are proactive. If you are working as a data scientist and you ask what happens if a data scientist and the team suggest some product idea, like what is the process? How bureaucratic is it? How much time does it take from an idea to going to production? If you ask these kinds of questions, you try to demonstrate your strengths. It’s also a way of emphasizing your personality traits, like proactiveness or stakeholder management, even in the last five minutes of the interview.

How a horse's ass determined the design of a space shuttle

Alexey: This is a great tip to use questions to highlight your own strengths. One last thing — this is something we promised to people who registered for this event. This is the teaser that you used. How a horse's ass determined the design of a space shuttle and what does it have to do with your CV? (1:04:24)

Oleg: This is a very famous story. The legend says that the distance between the rails on the railroad is the same as the width of a two-horse carriage. This makes sense because people used to carry goods and themselves on carriages. They did not have engines, they had horses. Then they applied the same size with the same tools when they started using railroads. Then several decades or a century later, a space shuttle was built. But they had to carry the parts of the space shuttle from the factory to the launch site. They could not do it by car, they had to do it by train. The details of the space shuttle had to fit the train. This is how the width of a two-horse carriage defined the design of the space shuttle — through this limitation. I don’t know if it’s true, but this is a very famous story. (1:04:49)

Oleg: So, every time I look at the CV, I think about this story. CVs in this current format — as a one or two pages of A4 paper — became popular in the late 20th century when everyone has got a printer and Microsoft Office. This was an easy way to hand them to the interviewer, to have this review of your experience on a piece of paper. This was the way people shared information before — there was no internet in that time. Now it does not make any sense because no one prints the CVs. I don’t even remember when was the last time I saw a printed CV. But the format is still the same. You can think about how other industries evolved in this time. Again, you can think about landing pages. How web page design has changed. At the end, a CV is a landing page. The goal of a CV is to show your experience, to help recruiters and companies see how relevant your experience is for a certain team. The format has not changed at all during this time. We just spoke about putting your picture, your age and your home address on the CV. These things are completely irrelevant. This is just an example of something when something archaic still defines how people apply for jobs now, as much as this two horses influence the width of a space shuttle.

Alexey: So, what you are saying is we should ditch our CV and build landing pages? (1:07:50)

Oleg: No. What I am saying is, I would think of a CV as a landing page. Something that can get your attention in the first few seconds and make you read further. It’s something valuable for you, for the company, for the recruiter. Maybe at the bottom somewhere you can add some details that are not as relevant for the job. But you can think of it as a webpage design. On the other side, you can think of a CV as future engineering. Hiring and applying for a job is classification. There is a binary outcome: you either get an offer or don’t. The decision is based on this prediction on how well you will perform at the job. Your past experience and your raw unstructured data. Your CV is the features. (1:07:56)

Oleg: What do we do with future engineering? We try to find future importance. We try to remove outliers. We try to clean up the data. Think about the signal to noise ratio. Put as much signal relevant for the job for a certain role, remove all the noise. If you think about models… My background is in personalization. Try to personalize the CV for every role you apply. Try to maximize this relevancy score between the job description and your CV. Don’t make it in the same way, it was in the 80s.

Alexey: Make sense. We still have quite a few questions but I suggest taking these questions to slack and answer them there. I’d like to thank you for joining us today and sharing your experience of what you learned from interviewing 500 data scientists with us. And thanks everyone else for joining us today. I wish everyone a great weekend and see you next week. (1:09:26)

Oleg: Thank you for joining me. Thank you for having me. (1:09:51)

Alexey: Goodbye. (1:09:54)

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