The Data Science Interview Loop
Data science interviews vary depending on company stage, size, and domain. Your recruiter should provide guidance on what to study for each interview. This lesson describes the most common data science interview stages:
- Recruiter screen
- Technical screen
- Statistics and experimentation
- Product sense
- SQL
- ML coding
- ML concepts
- Behavioral
- Take-home assignments
Check out our company interview guides, which describe the specific interview process for companies such as FAANG, TikTok, Microsoft, and more.
Recruiter screen
Time estimate: 30 minutes
The recruiter screen is a quick discussion to summarize the job expectations for the role and and assess whether you’re a good fit.
Usually, the recruiter will provide details about the entire interview process and give materials to help you prepare. There may also be an initial discussion on compensation to make sure you are in similar ballparks.
Technical screen
Time estimate: 30-60 minutes
Most common in large companies, the technical screen assesses your comfort with solving technical problems and communicating your results.
Interviewers are trying to quickly drop candidates from the funnel, so the questions usually have definite, correct answers that your code should match. You’ll receive 2-4 questions focused on SQL, statistics, machine learning, or Python fundamentals, such as this example question. Depending on the role’s focus area, these questions might be pulled from a pool of questions that are also asked to data analysts or software engineers.
To prepare for this round, practice the coding questions included in the SQL Interviews course and watch the mock interviews in the ML Concepts Questions course.
Statistics and experimentation
Time estimate: 60 minutes
There are typically multiple statistics rounds in the interview process. For example, you may have a technical screen about statistical concepts, and then 1-2 rounds in the final on-site interview.
One of these rounds (usually the on-site) may focus solely on experimentation, particularly A/B testing. Experimentation rounds assess how you would run an experiment for that specific company. You’ll discuss how you’d design the experiment, run the experiment to get results, anticipate challenges associated with any approach, and communicate end results to both technical and non-technical stakeholders.
To prepare for this round, review the Statistics and Experimentation Questions course, which contains modules on data preprocessing, probability, hypothesis testing, and A/B testing.
Product sense
Time estimate: 45-60 minutes
Similar to interview rounds for product managers, the product sense interview is particularly relevant for data science positions with a business or analytics focus.
This round will assess your ability to extract business insights and recommendations from data, through questions related to metrics, product strategy, and execution. Usually, interview questions come in the form of mini-case studies.
To prepare for this round, review the Data Communications course, particularly the Case Studies module. To supplement your product sense skills, you can also review the Product Management Interviews course.
SQL
Time estimate: 60 minutes
For most data science roles, the SQL interview round will involve questions that have 1 or 2 tables. You should be able to JOIN, do aggregates, conditionals (WHERE), and window functions. You should also have reasonable product sense to know what to calculate.
To prepare for this round, review the SQL Interviews course to understand syntax basics, aggregations, and window functions.
ML coding
Time estimate: 60 minutes
ML coding interviews are particularly relevant for data science roles that build algorithms and deploy models to production.
Rather than conceptually working through the problem, you’re showing the interviewer that you can take a problem and solve it through applied programming skills. For example, the interviewer may provide a dataset and ask you to write code to build a model and evaluate it.
To prepare for this round, practice solving different types of ML coding questions in the ML Coding Questions course.
ML concepts
Time estimate: 60 minutes
This interview round tests your conceptual knowledge of ML fundamentals. Expect questions like, “What’s your favorite ML algorithm, and why? How does it work? What are the pros and cons of this algorithm compared to others?”
In other versions of this interview, you might be provided a problem the company is solving (e,g, ranking, finding fraudulent transactions, etc.). Then you’re expected to solve the problem by building an ML algorithm, describing how it works and how you’d improve it, and listing its pros and cons compared to other algorithms.
This interview most commonly appears for data scientist roles focused on deploying ML algorithms and utilizing NLP or deep learning. Roles focused on product analytics are more likely to give you a problem where you’re expected to describe the key performance indicators (KPIs) you would measure, rather than building an algorithm.
To prepare for this round, review the ML Concepts Questions course, which contains ML mock interviews and practice questions specific to data scientists.
Behavioral
Time estimate: 30-60 minutes
The hiring manager usually evaluates your communication style and how well you work with other teammates. They assess your background and previous projects, how you work in cross-collaboration with other teams, and how you communicate across the company.
To prepare for this round, review the Data Communications course, specifically the Past Projects module. The course contains examples of how data scientists present past projects and best practices for communicating data projects effectively.
Take-home assignments
Take-homes exist to demonstrate execution. The question can help you prove your technical ability, your problem-solving ability, and show how you translate your thinking into action. Startups are more likely to give you take-homes because they are much more concerned with execution.
Most often, you’re given a dataset and a directional task (“improve the business”), but there are more and less defined versions of this challenge. Usually, you will at least be given a dataset.
To prepare for this round, review the Data Communications course, specifically the Take-home Assignments module. The module explains how to approach take-home exercises and how to structure the final output.