“Winging it” is killing your career. 5 tools I use to overprepare for Data Science interviews.

Hiring season is in FULL swing. It’s hot, right now.

If you’re getting interviews, you need to be converting them to offers.

Don’t “wing” your interviews. You need to go into those interviews prepared… actually, over-prepared.

Here are my top 7 favorite tools for preparing for Data Science interviews:

1. Glassdoor for finding real interview questions

Glassdoor is my go-to to get questions from past candidates.

Search your target company then filter by role, and look at the interview questions posted. It’s okay if some of the questions are old (like even 7+ years old is fine), because I find that companies don’t often change up their interview questions.

What I pull from each thread:

↳ Exact questions asked (copy/paste them into a doc for practice)
↳ Interview format and timeline
↳ Any weirdness in the interview

2. DataCamp for brushing up on Statistics

Statistics is particularly important for Data Scientists roles (vs. other Data roles). Start with the basic Stats concepts and ML models.

If you’re interviewing for a Product Data Science roles, make sure you’re also brushing up on your AB testing & experimentation frameworks. You’ll 100% be tested on this.

3. Interview Master for practicing for SQL and Python interviews

I don’t care what data job you’re going for, you need to know SQL.

I’ve interviewed for Data Science roles at 20+ companies, and I had a SQL interview at almost every single one. I cannot overstate this: SQL is the most important skill for a data professional.

When you practice for SQL interviews, practice in the context of solving real-world problems.

You don’t want to be doing questions about a murder mystery (unless you’re interviewing for a detective company, I guess).

Interview Master has 200+ SQL questions you can also solve with SQL and Python.

All based on real companies, real products, real problems.

4. ChatGPT to prep for case study interview

I used to be an interviewer for Meta and Amazon. Case study interviews were typically the make-or-break, on whether or not someone gets an offer.

If it helps you prepare, common case study interview topics include:

→ Metric definition
→ Opportunity sizing
→ Experiment design
→ Metric investigation
→ Model design and testing

I use ChatGPT to prep in 2 ways:

• Developing a deep understanding of the product and metrics

• Simulating case study mock interviews, and getting a thorough evaluation on how I can improve

5. Claude for researching the company

I like to use Claude to research the company, particularly using the web-search feature.

Make sure you’re turning on Web Search, so it’s looking for specific articles and resources and so you can validate that the info is accurate.

What do I search for? Everything about the company, industry, and competitors:

↳ What are their main opportunities and risks
↳ Who are their biggest competitors
↳ What is the latest industry news

I use this information to anticipate the questions I might get asked, AND to craft the questions that I ask my interviewers.

6. LinkedIn for researching my interviewers

Yes, it might feel a little creepy, but this makes a difference in how you interact with your interviewer.

I look for similarities between me and my interviewer. For example: have we lived in the same city, worked in similar industries, gone to the same school, or even share hobbies? Finding those overlaps helps me form a connection that isn’t superficial.

Really, we’re trying to make affinity bias work for us here. We want to be memorable.

Of course, don’t be creepy about it. Show you did some homework, but don’t be a stalker.

7. This GitHub repo for behavioral interviews

Ya’ll know what behavioral interviews are and how to prep for them. I don’t think I need to say more.