How to Survive Your Data Science Interview

There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every company

Interviews are scary as shit. You sit across the table from someone who has the power to grant you income and measure of security. They hold your future in their hands. You have to make them like you, trust you, think you are smart. There are fewer situations in life that induce greater anxiety. Luckily, there are some things you can do to make it a little gentler on you.

There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every company. To some it means PhD statistician. To some it means proficiency in Excel. To some it means machine learning generalist. To some it means being handy with Spark and Hadoop. Read job postings carefully for specific skills, tools and languages. A well written posting will give you a lot of insight into what they are looking for.

You have a limited amount of time to prepare. This is your budget, and you want to spend it so as to get the biggest bang for your buck. You have a few options:

  This is only enough time to dust off things you already know. Compare your current level of comfort to the job posting. Are there some skills you haven’t used in five years? Some terms you don’t recognize? Where are your biggest gaps? Spend more of your time on these.

Here’s how you can loosen your foundational skills in their holster:

If you need ideas for practice problems, GlassDoor and LeetCode are helpful resources.

You will get the biggest benefit if you do all of your practicing OUT LOUD. Explain your answers to your cat, or to an empty chair. Use a pen and paper, or better yet, a whiteboard. These help recreate common interviewing environments in small ways so that when you get there it feels a bit more familiar.

Don’t panic if you encounter topics and tools you’ve never heard of before. Many job postings are written as wishlists. They sound like a fifteen year old describing their perfect mate—a billionaire celebrity-lookalike winner of the Nobel Prize in physics and the Peace Prize. Those are all fine attributes in a partner, but most of us would be pretty excited about finding one or two of them. No one has them all. In my experience, the candidates hired are strong in some of the points listed in the post, but not necessarily all of them.

Also, don’t forget to leave time for a fifth area: learning about the company. Visit the their web page. Get a sense of how they make their money and who their customers are. Read their engineering blog to learn what tools they use and how their infrastructure is built. Learn the name of the CEO. If you are lucky enough to get your interviewers’ names, Google their professional activities. Learn about their research interests. Get a feeling for what matters to them.

  If Pocket Plan preparation is a single pancake, Standard Plan is the tall stack – the same process, repeated, each pass going a little deeper than the last. You work more of the same example problems and read through more of the same references. Where the Pocket Plan only gives you enough to time to dust off your skills, the Standard lets you put a sharp edge on them. You can learn about things that you’ve heard of but never absorbed.

After your first pass through the Pocket Plan, take a step back and look at where you feel the least prepared. For instance, if the job posting says “SQL required” and the recruiter told you that you will be asked a couple of SQL questions, but you don’t know any SQL, that’s a gap. Make a wishlist of things you would like to dig into deeper and start with the biggest gap. Then go back to the Pocket Plan and allocate your time to practice problems and reference reading accordingly.