Top 5 Pinterest Data Scientist Interview Questions + Guide in 2024

Pinterest, Inc. is a social media web and mobile application company founded in 2009 and headquartered in San Francisco, California. Aspiring candidates preparing for a Pinterest Data Scientist interview should be aware that Pinterest’s platform generates massive amounts of data daily, driven by its millions of active users who utilize the platform to discover and save information through images, GIFs, and videos (known as Pins).

With the amount of data Pinterest has, Data Science is at the core of Pinterest products and services. Data scientists at Pinterest leverage the most advanced analytics tools and machine learning models to make sense of this data for guiding business decisions.

In this guide, we’ll discuss strategies for acing the data scientist interview, along with Pinterest data scientist interview questions to better help you with the interview.

Even now, Pinterest is still a growing company with many teams and departments working on key features, products, and services to improve customer experiences.

The data science team at Pinterest occasionally collaborates with other teams to design experiments around almost every user-facing feature to help make sense of the huge customer data generated daily, driving decision making and providing business-impact insights. As a result, data scientist roles at Pinterest are hugely determined by the assigned team. However, available data scientist roles at Pinterest span experimentation and statistical modelling, basic business analytics and data visualization, machine learning, and deep learning theories.

Required Skills

Pinterest hires only qualified Data Scientists with at least 3 years (6+ years for a lead role) of industry experience in relevant data science projects. Requirements for hire are very specific depending on the job role for the team, and as such, it helps to have specific industry experience that aligns with the role on the team.

Other relevant qualifications include:

• Advanced Degree (MS or PhD) in a quantitative field or related fields.
• 3+ years experience (6+ years for a senior role) of industry experience and a proven track record of applying statistical methods to solve real-world problems using big data.
• Industry experience in both online and offline experimentation.
• Experience managing and analyzing structured and unstructured data with SQL, R, or Python, and using software packages like SPSS, STATA, etc.
• Extensive experience with applying deep learning methods in settings like recommender systems, time-series, user modeling, image recognition, graph representation learning, and natural language processing.
• Experience with learning from ranking labels (i.e., triplet learning, metric learning, etc.) and deploying ranking models (i.e., learning-to-rank).
• Ability to lead initiatives across multiple product areas and communicate findings with leadership and product teams.

Data scientist roles and functions at Pinterest run across a wide range of teams and fields related to data science. The title “data scientist” at Pinterest encompasses multiple roles and functions ranging from product focused-analytics to more technical machine learning and deep learning functions.

Based on the assigned team, the function of a data Scientist at Pinterest may include:

• Engineering (Offline Experimentation): Leveraging advanced data analytic concepts to solve key measurement challenges involving the offline evaluation of data, from fine-tuning measurement techniques to defining approaches for creating meaningful value measurements for new and existing new products.
• Engineering (Ads Experimentation): Designing and building models, mechanisms, and metrics to make sound product decisions through experimentation with the end goal of surfacing high-quality ads for every Pinner.
• Business Operation and Strategy: Leveraging business analytics to drive critical business insights for a better understanding of Pinners, Partners, and products.
• Ads Quality Ranking team: Applying experimentation, quantitative analysis, data mining and data visualization techniques to improve the quality and relevance of ads on Pinterest.
• Ads Intelligence: Developing machine learning models, systems, and features that help advertisers maximize the return on investment of ad campaigns on Pinterest through recommendations, tools, and insights.

The interview process starts with an initial phone screen with a recruiter or a hiring manager, and if all goes well, a technical screen with a data scientist or a data engineer will be scheduled. After passing the technical screen, you then proceed to the onsite interview, which comprises five back-to-back interview rounds with a lunch break in between.

Initial Screen

This is a 30-minute initial phone conversation with a recruiter detailing your technical background, past relevant projects, and a quick assessment of your skill sets based on your resume. Within this interview, the interviewer will also discuss the roles on the team and Pinterest culture with you.

Sample Questions:

• Tell me about yourself.
• Talk about one of your past work experiences.

Technical Screen

The technical screen is an hour-long interview with a data scientist, with a discussion revolving around a past project, the approaches you used, and how you solved certain challenges. There will also be some light SQL coding in this interview. Pinterest uses “Karat” for almost all their technical interviews, and the Data Scientist technical screening is also done using the shared screen Karat platform.

At a minimum, review our guide to SQL interview questions to prepare.

Onsite Interview

The onsite interview is the last interview stage for the Pinterest Data Scientist interview. It consists of five back-to-back interview rounds, split between a SQL interview, statistics and probability interview, one coding interview, and a behavioral interview. All interview rounds in the onsite stage last approximately 45 minutes, with a lunch break in between.

Pinterest Data Scientist interviews aim to assess candidates’ ability to design experiments for assessing product performance, build models at scale, and apply data science concepts to drive growth and provide business-impact insights. Therefore, data science interview questions are standardized and cover a wide range of concepts. Brush up on your knowledge of statistics and probability, hypothesis testing, time series modelling, A/B testing, experimental designs, SQL, and predictive modelling concepts.

Pinterest has an employee-focused ecosystem, which provides a friendly work environment for all. In a 2019 article, Pinterest was quoted as “the nicest company in Silicon Valley… The culture stands out from other high-growth tech companies where confrontation and debate are actively encouraged”. Culture-wise, Pinterest offers a really progressive work environment where employees (technical or not) can grow and thrive.

1) Give an array of unsorted random numbers (decimals), find the interquartile distance.

2) Write a SQL query to count the number of unique users per day who logged in from both iPhone and web, where iPhone logs and web logs are in distinct relations.

3) Your product manager noticed a dip in a specific metric. How do you go about investigating what may have caused the dip?

4) Write a query to create a metric that can validate and rank the queries by their search result precision. Round the metric (avg_rating column) to 2 decimal places.

5) Given two strings, string1 and string2, write a function max_substring to return the maximal substring shared by both strings.

See more Pinterest data scientist questions from Interview Query: