Demystifying Data Science Interviews

Factors and interview process for data science

In this article series, we will look at various factors to consider as you look for your first role or next role in this exciting and dynamic field. The goal of this series is to provide you with a framework to approach your data science interviews, taking into account a variety of factors we will cover in this first post.

In this initial post we will lay the ground work by covering the factors to consider along with the overall data science interview process.

NOTE: Data Science in this article is a catch all to refer to all data professional roles including analysts, scientists, researchers and engineers.

Important Factors

When looking for our next adventure, we have the usual suspects — seniority, type of work, and salary. All very important, of course — I’m not here to dispute that one bit. Rather, let’s take a look at some other factors worth considering. The idea isn’t to cover every nook and cranny, but rather to provide a set of frameworks by which we can evaluate various opportunities.

Company Maturity

Company maturity is no more correlated to how long a company has been in business than a person’s maturity is related to their age! The question here is at what stage of building this business is the company in?

A start up in the early days is predominately focused on building a minimum viable product and getting to product market fit before funding runs out. It’s a race to survive!

A business of medium maturity has proven product market fit and is fully focused on growing via customer acquisition and building new features.

A mature large company has a mature product line and is focused on growing revenue by expanding into other product lines. These other product lines may lead it to operate under a diversity of domains.

A mature conglomerate has multiple mature product lines and a diversity of domains that it operates under.

Industry

Every industry has its own intricacies and dynamics. One thing that is common to all though is that software AND data is eating them up. There is lots of disruption across many industries.

Data is also creating new industries — Ezra is a company using data to detect cancer early.

Old industries are also not immune to the changes we are witnessing. They have noticed and are investing in ramping up their data capabilities. For example, Shell has created a Shell.ai residency programme.

Role of Data

The use of data isn’t limited to single use or purpose. Data can have varying roles within a company’s strategy and product roadmap.

Data could be the product or it could be the growth engine for an existing product.

Availability of Data

Just as the role of data can differ from company to company, so can its availability and how it is acquired.

Data can come from the product itself. Think of Facebook, Google, Amazon, and AirBnB who can gain insights from how their customers use their products to build better features or all together new products and services.

Data can be acquired. Think of Ezra or Clearview AI which get data either via partnerships or scraping websites. This collected data is brought together to build a data product.

Your Role within the Data Stack

Finally, we come to you, the reader :)

What role would you like to play in this exciting world of data? Do you want to sift through it to analyze patterns? Do you want to be at the foundations helping build the systems that move, clean and store it? Or do you want to build data products such as recommendation systems or voice assistants?

Perhaps you are at home doing research on the latest and greatest, hypothesizing and experimenting with new approaches?

Whatever it is that tickles your fancy, you are sure to find it in the hierarchy of needs for Data Science.

And that wraps up the important factors you should consider as you pursue various data science opportunities. These factors should help you to evaluate companies, understand where they are in the stack and make decisions on where you should consider interviewing next.

The Data Science Interview Process

Now, let’s go over the Data Science Interview Process at a high level.

  1. You apply to a role OR a friend/contact refers you OR an internal/external recruiter reaches out to you
  2. You have an initial non-technical interview to get an understanding of what the company is looking for, the culture, etc. On the company’s side, they are trying to understand what you are looking for in your next role and whether or not it is a fit.
  3. You have a technical screen — this can range from either a call with a technical person where you code in a screen-share, or it is a take home assignment
  4. On site/remote zoom calls — usually this is where you show up to the office and spend all day or half a day meeting with various team members. Alternatively this could be a series of zoom calls together or broken up over a few days or months. This is a mixture of behavioural, technical and cultural interviews.

What’s next?

This is the first post in a series that will help demystify data science interviews.

Next, we will look at a range of scenarios where people will consider different roles with different companies and industries. We will use the foundations we laid out in this article — the important factors and the high level overview of the data science interview — to shed more light in how the candidates can go about evaluating the opportunities they are pursuing.

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