How I entered the data science field (part 2) - Tailored approach to data science interviews, step by step

In part 1 of the “How I entered the data science field” series, I walked through the 2 pillars of data science roles: statistics and programming knowledge, as well as how to prepare to enter the field based on your expertise in those 2 pillars.

Here, in part 2, I’ll walk through what to expect, if you’ve received the good news that you’re getting an interview! This includes some time-saving tips for covering the huge variety of behavioral questions, how to make 10 minutes of research pay for itself many times over, and much more.

The first “gate”: HR or recruiter phone screen

Know the distinction between HR and hiring manager

For mid to large sized companies, HR or non-technical recruiters (aka talent acquisition) usually do the initial filter of resumes, and then have a call with candidates that seem qualified. After the conversation, if they feel the candidate is a potential fit, they might pass it on to a hiring manager.

Back in 2017, I was confused as to the difference between HR and hiring manager. Now, I know that hiring managers are usually who you’ll really be working with, and report to, whereas the HR usually conducts initial screening and other types of administration such as coordinating interviews. Once again, this is assuming a mid to large sized company with established departments - I will not be going through startup specific information in this article, although most tips should still be applicable.

Showcase your communication skills to pass the first “gate”

Here is a general rundown of how the first HR or recruiter phone screen usually goes like:

  1. They ask you to introduce yourself
  2. They ask you about your experience in some technologies (they usually compare your answers against the job description or a list the hiring manager provided)
    • Example: “Do you know how to use Tensorflow?”
  3. They probe you on items on your resume
    • Example: “Tell me more about your Reddit scraping project.”
  4. They ask you some behavioral interview questions
    • Example: “Tell me about a time you demonstrated leadership.”
  5. They usually ask why you are interested in their company or this role

Since the HR won’t be working with you in your day to day, what it takes to get past this first “gate” isn’t necessarily even technical skills, but rather, communication skills.

I wish I were kidding when I say people actually fail this round because of avoidable reasons such as being nervous, or being unable to articulate their answers with less technical jargon. This is because your recruiters may come from many types of educational backgrounds, so you need to be able to communicate at varying levels of technical detail. If you can convey, in a way that the HR or recruiter understands, just why your skills can be valuable, they will happily go to bat for you and recommend you to the hiring managers.

Full guide of tailored approaches to clear the phone screen

Introduction: “Tell me about yourself”

Experience in [technology], [machine learning technique] or [programming language]

Probing about your experiences listed on resume

Behavioral interview questions

Why are you interested in this company or role?

That’s a lot of information, as well as a lot to prepare for, but the good news is that the preparation in this round will pay for itself in future interview rounds. Though, if you don’t feel like you have time to prepare for all of those questions in detail, here are the time-saving methods that I found to greatly increase the odds of passing this round:

If possible, find a friend to practice with - I was lucky that my friends spared their time to hear my elevator pitch - you’d rather stutter in front of your friends first, than the interviewer!

Technical / coding round(s) with hiring manager and future coworkers

If you wowed the HR during the initial phone screen, in a mid or large sized company, you’ll likely be invited to interview with the hiring manager afterwards. My preparation strategy for these further rounds involved much more technical detail, for example reviewing any statistical modelling I mentioned anywhere on my resume. Since many of those projects were done some months or years ago when I interviewed, it was only natural that I needed to spend time brushing up on details!

The good news is, a lot of the preparation for the previous round can help you with this round as well. Some questions, especially the behavioral ones, might even be similar or identical, but the later rounds are a good time to toss in more technical details in your answers.

Tailored approach to hiring manager interviews

Now, in my experience, I found that technical interview rounds had much more variation than the phone screens. This is because each company, or even the team itself, may focus on different types of data science. Hence, this is where the research you did about the company and team will also come in handy.

If the role is entry level or the description isn’t specific enough, it’s always a good bet to review some common algorithms like random forest, logistic regression, K-means clustering, etc. (In the interview for my current role, I did get asked to explain all those algorithms verbally).

In general, I prioritized theory and technical details of basic machine learning algorithms, and those used in projects I specifically had mentioned on my resume. If you mentioned using CNNs on a project you did on your resume, be prepared to explain your approach, and its pros and cons. Of course, if your job description literally says they’re looking for someone strong in NLP, you had better prepare for techniques in the specified topic too.

However, if no one has yet told you what fields the role focuses on, from the company’s mission or information about the hiring team, you can make educated guesses on what kind of algorithms to review. If this happens to be the case, I recommend asking the HR or hiring manager, with more details in the following section.

Technical rounds vary wildly - gather as much information as you can

Now, regarding coding interviews, I have heard wildly varying experiences. There are some that are 90% coding and 10% asking about past experience or algorithms; there are some interviews that are the opposite way around.

For example, my own experience interviewing candidates is that we’re looking more for problem solving skills and proficiency in Python, and we do not ask LeetCode style coding questions, which seem to have risen in popularity. Hence, I won’t comment on too much detail about coding portion preparation since it could vary too much role to role, and I don’t want to misguide anyone.

It is possible to make an educated guess from what you understand of the team’s day to day usage of the 2 pillars of data science: statistics and programming - if the team’s work relies more on statistics knowledge, it’s possible they ask more of the former, and vice versa. I elaborate more on the 2 pillars in part 1 of this series.

I suggest you get as much information from the hiring manager as possible; if you don’t know who that will be at this stage, ask your HR or recruiter contact. If the HR doesn’t know, you can ask them to ask the hiring manager on your behalf. I found that the HR folks are very nice about this and do try to help you - part of their role is to give the candidate sufficient information, after all.

Immediate next steps for technical interview preparation

One last reminder, which I have mentioned before, but is worth repeating: I really suggest you frame your answers based on the company’s mission and what you know about the team. Think about it this way: if my team does machine learning for customer personalization, but nothing about the candidate’s experience seems to be able to contribute to our team’s mission or techniques we use day to day, it makes it harder to hire the candidate.

That’s why it pays off to tailor how you describe your experience to what you know of the interviewers, because they simply don’t know perfect information about the candidate. In reality, between two candidates with the exact same skill set, the one that can better make their case on how they can contribute to the team, will be much more attractive.

Summary and immediate interview preparation steps

There is a large amount of resources online about the data science interview process, which can be overwhelming. I was also in that situation when I started the job search, and I definitely can relate - which is why I shared the above details and tips. I hope that all that I learned can help you!

If you have some time before the applications and interviews, as mentioned in the first part of this blog series - the key is to bring your machine learning theory and programming both to a baseline level. For example, if you have 0 knowledge if one of them, bring it up to a 2 or 3.

If you have interviews booked, the immediate actions are:

  1. Prepare your elevator pitch self-introduction
  2. Research the company and role
  3. Prepare a handful of stories that are each able to answer multiple behavioral questions
  4. Review technical details of your resume, and focus on skills that the team or role need
  5. Review code implementations of past projects or LeetCode

And that’s it for this blog series. I hope that it was helpful or interesting, whether you are job searching or not. If you happen to be going through interviews at this time, I wish you all the best!

Additional resources

How I entered the data science field (2 part series)

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