Interviewing for Your First Data Scientist job: What to Expect and How to Prepare

Interviewing for Your First Data Scientist job: What to Expect and How to Prepare

In this section, we’ll discuss the typical stages of the interview process. Note that while most companies will have similar stages, the ordering may differ.

If you feel overwhelmed about how to start your journey to become a data scientist, you are not alone. When you search for “data science interview”, you are presented with endless pointers, including topics in Python, R, statistics, A/B testing, machine learning, big data. You get recommendations to read countless books. Embarrassingly, I have given similar broad advice to others.

In reality, you don’t have to prepare for everything to get your first data science job.

In this post, we will teach you about four key areas:

  1. The types of Data Scientist roles
  2. The types of interviews you should prepare for
  3. What to expect during the interview process
  4. What interviewers are evaluating

Let’s dive in.

Table of Contents

One pain point we often hear about is that job titles are confusing.There are many titles, such as Product Data Scientist, Machine Learning Data Scientist, Data Science Engineer, Data Analyst, and the list keeps growing. If you are not familiar with the industry, it is difficult to know which positions to apply for.

In general, there are four types of roles: Analytics,** Statistics, Data Engineering, and Algorithms**. This categorization is based on large companies with mature Data Science teams (eg., Facebook, Lyft, Airbnb, Netflix).

Above, we describe each role with their specialization and example titles. Below, we further elaborate.

  • Analytics. This role drives business impact by making recommendations based on data insights. Responsibilities include helping stakeholders make data-informed decisions, performing exploratory analyses, defining business metrics, and making data visualizations (eg., dashboards).
  • *Statistics. *This role identifies opportunities to scale experimentation and implements statistical approaches (eg., causal frameworks) to solve business challenges.
  • Data Engineering. This role builds scalable data pipelines to enable data-driven decisions, typically for data savvy consumers (analysts and Data Scientists). This role is similar to a typical data engineer but is usually embedded in a data science team rather than focusing on serving a broader set of stakeholders (such as engineers and product managers).
  • Algorithms. This role creates business value by developing statistical, machine learning, and optimization models. Often, one performs exploratory data analysis to obtain a deeper understanding of the business problem and productionize models.

Even though each role may seem unique, there are often overlaps in responsibilities. In reality, it’s common to wear hats from multiple roles depending on the team composition and business needs (especially in smaller companies). Learning about the types of responsibilities and projects of the role is important for you to learn early in the process (by asking the recruiter or hiring manager) so that you can get a sense of your fit for the role.

The diagram below shows the distribution of different roles on the job market. This result is based on ~1,000 full-time data science job openings that were posted on LinkedIn September — November 2020.

It is clear that the dominating role is Analytics, while Statistics has the least positions. As such, if you’re early in your data science career, Analytics would be a great starting option.

That being said, which role most closely aligns with your skill set, interests, and role availability? Pick one and focus your attention on the skills you’ll need for that role, which we’ll share more about later in this post.

Next, let’s walk through the process you should expect during the interview process.

Interview Process: 5 Stages

In this section, we’ll discuss the typical stages of the interview process. Note that while most companies will have similar stages, the ordering may differ.

Below, we’ll dive deep into each of the stages, including the company’s goal, and what to expect. In the following section, we will go into more details about the types of questions asked for each type of interview.

data-science-job deep-dives

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