Pinterest Data Scientist Interview Guide

The gist

Pinterest is built on creativity, inspiration, and “building a more positive internet” through their social media platform. They foster a culture of collaboration and belonging both within the company and with their users. With over half a million active monthly users, there’s no shortage of opportunities for data scientists to make an impact at Pinterest.

Despite the company’s growth in popularity since its inception in 2010, it has remained a fairly small company, giving individual contributors the chance to work on high-impact, high-visibility projects. Since the company has stayed small and gritty, they’re really invested in their hiring process. Interviews for data science hopefuls include a rigorous tech screen and a five-part on-site that put your analytical and problem-solving skills to the test.

To land a data science role at Pinterest, you’ll need a deep understanding of data science fundamentals and strong communication skills—. Mid-level and lower-level candidates can get away with a little less technical knowledge, but you’ll need solid knowledge of the basics (think forming hypotheses and experiments). Brush up on your SQL skills for the coding round, and make sure you’re comfortable with either Python or R.

One thing that sets Pinterest’s hiring process apart from other tech companies is their specialty-focused round. During the interview process, you get to choose a data science specialty you’re interested in: statistics, machine learning, or forecasting. Your choice shapes the conversation for one of your final round interviews and may also impact the types of questions you’re asked during the other final round discussions. This is a testament to the way Pinterest values the work data scientists do, ensuring they’re fully baked into the company’s engineering processes.

What does a Pinterest Data Scientist do?

Data scientists at Pinterest apply their analytical skills and their expertise in quantitative modeling, experimentation, and algorithms to solve business problems. If you become a Pinployees, you can expect to:

• Prototype ML models to assess potential business opportunities
• Build scalable pipelines to provide product development insights
• Define statistical methods to improve development processes
• Collaborate with cross-functional stakeholders from Engineering, Product, Design, etc.

Cross-functional collaboration is key to your success as a data scientist at Pinterest, and it’s an important piece of your final interview round. The company is looking for people who aren’t afraid to get a little hands-on with engineering efforts or other interdisciplinary projects. Some senior data scientists at Pinterest have even had a hand in shaping the company’s content strategy through their work.

The company supports data scientists at all stages of their careers, with a leveling system ranging from L3–L6. Compensation packages on average for the 3 levels we found data for:

• Data Scientist (L3-L4): $205K-$240K
• Data Scientist II (L5): $351K

Before you apply

• Take a look at some interview questions Pinterest asked recently.
• Spend time deep diving into SQL question and Python question best practices so you’re ready for the tech screen and coding rounds.
• Think about how you’ve worked with cross-functional teammates in the past, and how that experience might help you at Pinterest.

Interview process

The data science hiring process at Pinterest has three stages that increase in complexity, length, and difficulty as you progress:

1. An initial screen with a recruiter to get a sense of your experience and give you a taste of the company’s culture.
2. A technical screen with a brief coding assessment and case study.
3. A final round consisting of five on-site interviews ranging from coding to hiring manager discussions.

1. Recruiter screen

The first round is a standard recruiter screen. You’ll speak with a Pinterest recruiter about what you’ve been working on in your current role and what you’ve done over the last few years.

This round is light and the recruiters are friendly, so don’t worry about getting grilled with any tech-based questions (yet). However, you should be ready to describe one impactful project you worked on. Here, the recruiter is specifically trying to tease out what your tech stack looks like and which tools you’re most familiar with.

During this round, expect questions like:

• Tell me about your previous work experience.
• What scripting languages are you familiar with?
• Tell me about a meaningful project you worked on.

2. Technical screen

The technical screen is a 45-minute phone interview split into two parts: coding (with a standard SQL question) and a case study related to a sample Pinterest business problem.

For the coding portion, you’ll be given two to three SQL questions that increase in difficulty. The expectation isn’t that you’re a coding expert, but that you understand the fundamentals well enough to suss out any faulty logic. Don’t worry about things like percentile-based, LEAD, or OLAP functions—the tech screen won’t go that deep.

Topics to study for this portion include:

• Aggregations
• Joins
• Windows functions like RANK
• Pandas

You can expect to see questions like:

• Write an SQL code that extracts certain information from three tables.
• Write a query to retrieve specific columns from a table with filtering conditions.

For the case study, your interviewer will walk you through a business problem and ask you to define any metrics you’d use or experiments you’d design to assess the problem. The team is looking to see your problem-solving thought process and how you approach scenarios when you’re given only high-level details. You’re expected to drive the conversation here, and the follow-up questions the interviewer asks are tailored based on how you steer the conversation.

You might see a question like:

• Retention is going down by 10% on a week-by-week basis. As a data scientist, how would you analyze this scenario?

Be ready to showcase your ability to start from the basic principles of forming a hypothesis, choosing a randomization unit, randomizing users, etc., and then define your North Star metrics.

Topics to study for this portion include:

• Forming a hypothesis
• Defining metrics
• Asking clarifying questions

3. Final round

The final round is the most intensive part of the hiring process, with a five-part on-site consisting of:

1. Coding
2. Analytical problem-solving
3. Specialization
4. Hiring manager
5. Cross-functional

Interview questions

Coding

The coding on-site focuses on SQL and algorithms. It’s not as intensive as a software engineering coding round, but you can expect basic data structure questions. Like the technical screen, the questions increase in difficulty as you progress. There are two SQL questions, and the second is significantly more difficult than the first.

For the algorithm portion, you can choose whether you want to use Python or R. Just be aware that most problems are designed for Python, so implementation may be more difficult in R.

You may see questions like:

• Calculate the statistical significance of a difference between two groups in an A/B test.
• Justify a string of text given character length.
• Count the number of clicks for each domain and subdomain.

Analytical problem-solving

This is a 60-minute round that focuses on a high-level business case. Interviewers talk you through the business case using a slide deck with various prompts. The goal of this round is to assess your product intuition and understand how you approach problem-solving using metrics. As you move through the prompts, you’ll have to frame a hypothesis or design an experiment to figure out the next steps.

Aside from problem-solving, communication is the most important element of this round. It’s critical that you ask follow-up questions here to get all the information you need for your hypothesis.

You might be presented with a scenario like a feature enhancement at Pinterest and asked questions like:

• What hypothesis is driving this change?
• Define use cases where this change might be beneficial.
• How would you design an experiment to test the impact of this change?

Some candidates have also had questions like:

• Pinterest WAU is up 5% but email notification open rates are down 2%. Figure out why.

Specialization

After you pass the technical screen, you’ll choose your data science specialization. Then, during the specialization portion of the final round, you’ll interview with Pinterest team members who share your specialty. This is a 45-minute round, and though the structure varies depending on your specialty, you can generally expect three questions, starting with a high-level case study that tests your specialty knowledge by asking you to work your way through a specific scenario. This case study is more standard than the case study you’ll see in the analytical problem-solving round.

Case studies are presented using CoderPad, and you’re encouraged to jot down your thoughts and calculations in CoderPad to show your thought process. This also gives interviewers a chance to redirect you if they see you’re heading down the wrong path.

For an ML-focused interview, expect to apply ML models to solve practical Pinterest business problems. For a statistics-focused interview, expect an emphasis on experimentation, causal inference, and high-level statistics. You may be given a statistical scenario and asked questions like:

• How do you choose the right population?
• How would you come up with the right sample size?

You might also be given a hypothesis-testing scenario to assess your understanding of specific tools like z-tests and t-tests. The goal of the business case for a stats specialist is to see whether you can develop a test statistic, calculate its value, and determine whether to reject the null hypothesis or fail to reject it.

Topics for statistics specialists to study:

• Hypothesis testing
• How stats are computed
• z-tests and t-tests
• P-values

The final question for this round is a causal inference scenario that you can’t really design an experiment around. Instead, you’re expected to make inferences based on observational data. Make sure you’re comfortable with a few causal inference approaches, like difference-in-difference (DiD) or the synthetic control method (SCM).

Behavioral

The hiring manager and cross-functional rounds are both behavioral, ensuring you’re a good fit for the team and the work style at Pinterest. The hiring manager screen is an informal conversation that focuses on your experience and gives you some insight into the team structure, the kind of work you’ll do, etc.

In the cross-functional round, you meet with a cross-functional stakeholder like a PM or engineer. They’ll assess your experience and ability to work with interdisciplinary Pinployees by asking questions like:

• Tell me how you work with cross-functional teammates.
• How have you handled challenges working with other departments?

Additional resources

• Check out Exponent’s Data Science Interviews course to cover all your interview prep basics.
• Practice some coding questions, focusing on SQL and Python.
• Make sure you’re comfortable with all the data science fundamentals, like designing experiments.

FAQs about the Pinterest Data Scientist interview

How should I prepare for a Pinterest Data Scientist interview?

The nice thing about data science interviews at Pinterest is that they’re not expecting you to be an expert in everything data science—but they do want to ensure you have working knowledge of the fundamentals, like developing a hypothesis and an experiment. Brush up on SQL and Python, be comfortable discussing ML models at a high level, assess your specialty expertise, and be ready to ask clarifying questions.

How much do Pinterest Data Scientists make?

Data is readily available for Pinterest Data Scientist total compensation, ranging from L3 ($157K) to L5 ($351K).

How long is the Pinterest Data Scientist interview process?

On average, the Pinterest hiring process takes 3–4 weeks from start to finish.