Spotify Data Scientist Interview Guide

The gist

If you want to be a data scientist at Spotify, expect interviews that emphasize the following:

• Solving practical problems
• A strong product focus
• Engaging, conversational discussions
• Friendly, approachable interviewers

Spotify wants data scientists who are passionate about music, light-hearted enough to fit in, and, most importantly, excellent technical communicators to non-technical and technical people alike.

Spotify values autonomy. It encourages individuals (not teams) to take ownership of tasks and intentionally reduces processes. Getting things done at Spotify requires more social interaction and collaboration across teams, so they need excellent communicators.

The hardest part of Spotify’s interview process is a presentation to a panel of interviewers. This round is where the weak candidates–especially the ones who looked great on paper–tend to out themselves. It’s the part of the process where rejections happen most decidedly.

The most common reason for rejection is poor technical communication. To win, speak so that an intelligent 18-year-old high schooler with no data science background could understand you.

What does a Spotify data scientist do?

Spotify’s data scientists work specifically in a product data science model, which means they work with PMs and engineers to run experiments, define the roadmap, and drive data-informed decisions for the business. There’s less focus on building ML models or deploying ML models to production (at Spotify, that would fall under an ML engineer).

There’s a focus on experiments and A/B tests, such as utilizing statistics and causal inference to detect causal effects when running experiments is infeasible.

You’ll work with a standard tech stack of SQL and Python and use Google Big Query for data warehousing. Suppose the amount of data gets too much to manage. In that case, data scientists often hand off that data to an analytics engineer (these folks are the intersection between data scientists and data engineers) to analyze and gather insights.

The prerequisite for becoming a data scientist at Spotify is hands-on experience synthesizing insights from data using tools such as Python, BigQuery, SQL, and Tableau.

Spotify prefers candidates with a strong understanding of experimentation, statistics, and machine learning for decision-making. Most critically, Spotify wants candidates who can present technical findings with clear product or business implications.

There are seven levels a data scientist can reach at Spotify. An employee-friendly aspect of their equity compensation is an equally split 3-year vesting schedule. Here are the numbers for total compensation for the first four levels, on average:

• Associate data scientist: $142k
• Data scientist I: $164k
• Data scientist II: $176k
• Senior data scientist: $204k

Before You Apply

1. Make technical communication your most obvious strength–ideally, take a public speaking class. Another way to improve communication is to practice interviewing with peers.
2. Develop some opinions about music–specifically, what trends you’re interested in. And think about what makes a playlist successful. If you’d rather get targeted feedback to sharpen these ideas, try a coaching session from a Spotify data scientist.
3. Spotify's most common hire is a senior data scientist, and it’s the most common position applied to. Make sure your resume doesn’t hinder you from standing out.

Interview Process

We’ve compiled a typical interview process based on conversations with Spotify interviewers.

Spotify requires some interviewer training. However, there isn’t much oversight, and interviewers can ask whatever questions they want. Most interviewers ask the questions that are suggested to them.

Comparing the philosophy of technical rounds, Spotify and Google primarily care about your thought process, whereas Meta and Amazon care mainly about your results.

Spotify has a team-independent process, so all candidates go through relatively the same loop.

Applicants generally describe four interview stages:

• A recruiter screen to ensure you meet the minimum requirements for the role
• A technical screen split between essential SQL, python, and statistics
• A hiring manager round, which is primarily behavioral
• A final-round interview, mainly to present your case study

Recruiter

Like in most recruiter screens, prepare to talk about your experience related to the items on the job description, what’s motivating your search, and how much money you’re looking to make.

Sample questions include:

• Walk me through your resume.
• Why did you apply to Spotify?
• What are you looking for in your next role?
• What are your compensation expectations?

Technical

This one-hour Coderpad round is split between SQL, Python, and statistics. The SQL portion involves basic SQL and often won’t ask for functions as complex as window functions.

The coding portion typically involves problems with easy-to-medium data structures and algorithms. It may also include questions on Pandas and Numpy.

Poor communication is among the most common reasons for rejections, especially at the senior level.

Say you get asked in the stats portion about p-values and deliver an answer that isn’t clear enough for a non-technical person to understand; you will fail even if your answer was technically correct!

At Spotify, knowing the answer isn’t enough; it must be communicated so anyone can understand.

Sample questions include:

• Write a function that would change the input of one column in Pandas.
• The Fizzbuzz problem.

Hiring manager

The hiring manager round at Spotify is mainly behavioral and may include a small case study question.

This round is the only 1:1 time you have with the hiring manager in the whole process. You’ll only see them again when you present your case study. This prepares senior-level data scientists to work autonomously.

Sample questions include:

• Why Spotify?
• What’s your philosophy on experimentation?

Final round

Before your final round interview, you’ll get a week to complete a take-home assignment. The prompt of this case study is: What makes a playlist successful?

Then, at the onsite, you’ll present your findings to a small group of interviewers, including your hiring manager. In addition to that presentation, you’ll have rounds with a product manager, a user researcher, and another data scientist you’d work with.

Spotify’s obsession with communication is important at the rounds with the different stakeholders. The interviews are mainly conversational but on technical topics. You probably won’t code or write queries onsite.

Instead, expect to talk about your work and how you’d approach hypothetical and open-ended data science problems.

Case study presentation

This is the crux of the final round at Spotify. For the most part, this is where candidates earn a pass or fail. You present to a room of ~5 people, and since it’s all about technical communication, it’s Spotify’s highest weighted round.

Interview Questions

Behavioral

Known for its friendly culture, Spotify has a relatively easy behavioral round. As a Swedish company, its country of origin shines through its culture: warm people who strive for consensus.

Spotify tends to hire friendly people, but they don’t have a rigorous culture screen. In this way, Spotify is more like Microsoft and less like Amazon. Interviewers aren’t looking to assess a candidate's signal of specific traits; instead, they generally evaluate liked traits: positivity, ownership, and Spotify’s favorite–clear communication.

Interviewers don’t assess specific traits; instead, they assess generally liked traits: positivity, ownership, and Spotify’s favorite–clear communication.

Questions like:

• Why do you want to be a data scientist?
• Tell me about a project where you worked with multiple stakeholders and dealt with conflicting priorities.
• How do you work on a team? What are your preferences?

SQL + Python

These are table stakes for the average data scientist’s toolbox, and it’s no different at Spotify. These are far from the most grueling Python or SQL rounds in tech.

Instead, Spotify opts to ensure you can do basic data structure and algorithms or know Pandas and Numpy. They only assess essential SQL.

The Python portion is more often failed, but it seems this is due to the commonality of day-to-day work with Python over SQL for most data scientists.

SQL topics to study:

• Joins
• Aggregate functions
• Analytical functions
• Set operators
• Subqueries

SQL sample questions include:

• Write a function to apply a markdown rate to certain song streams.
• For this data set, find the most popular track and the day it had the most popularity for each track.

Python topics to study:

• Pandas data manipulation
• Solving Pandas problems
• Numpy data manipulation

Sample questions include:

• Write a Python function to apply a markdown rate to certain song streams.
• Use Pandas to filter this dataset to X rows and aggregate Y metrics.

Statistics

Statistics are a focus at Spotify because the engineering culture is so big on experimentation and A/B testing. Ideally, you can test for everything, but when you can’t, you have to use statistical methods to deduce what a test might bring.

Topics to expect:

• Regression
• P-values
• Applied ML cases

Sample questions include:

• How do you deal with a class-imbalanced dataset?
• What is a p-value?

Product Manager

In the product manager round, whether you’re discussing your work history or their hypothetical product, spend a lot of time reviewing small product changes that yield significant results.

Interviewers will eat up every second of it. For example, “If we gave people the ability to ‘thumbs down’ songs, it might cause [result] based on [rationale].”

Sample questions include:

• How do you approach scoping new work? How do you work with PM stakeholders?

Experimentation

Another data scientist usually conducts these rounds. There’s a large focus on A/B testing, and this round is more likely than the tech screen to determine your level.

So, expect the prompts to be ambiguous.

Sample questions include:

• Look at these A/B test results. Are these findings significant?
• How do you make a new column in Pandas that is an aggregation of other elements from other columns? How would you visualize this data? What type of modeling would you use to predict X?
• Let’s say you’re on the ads team. The product manager says they want to increase ad load for a specific set of users. How would you work with the PM to decide whether to launch this, and how would you know if it was a successful change?

User research

UX researchers usually conduct these rounds. Spotify, and its core product, love personalization. Recommendation engines are core to Spotify's business. If you’re unsure what to discuss and have some relevant knowledge, dive into personalization or recommendation engines.

Sample questions include:

• What’s a trend in music you’re interested in?

Additional Resources

• Check out this collection of Spotify resources.
• To get your chops up, try this loved-by-users data science course.

FAQs

What can I expect from my interview at Spotify?

A focus on how you communicate. Plus, your ability to explain your approach to ambiguous data science problems. As well as some basic SQL and Python, and some rounds with stakeholders.

How much do Spotify Data Scientists make?

On average, you can expect these ranges for total compensation:

• Associate data scientist: $142k
• Data scientist I: $164k
• Data scientist II: $176k
• Senior data scientist: $204k

How long is the Spotify data scientist interview process?

This differs from one candidate to another. But expect 4–6 weeks, on average.

How should I prepare for a Spotify data scientist interview?

Sharpen your technical communication. Develop some opinions on music, specifically trends and what makes a playlist successful. Dig into SQL and coding. Then, do more communication practice, ideally in front of a room of strangers.