Real, anonymous reports from people who interviewed for Data Scientist at Pinterest, newest first and distilled into what to expect across the loop.
After the online screening, I got sent an OA that felt brutally paced. The whole thing took about 70 minutes and opened with 7 multiple-choice questions that were manageable. After that came a single LeetCode-style medium, then two ML-focused coding tasks that had “from scratch” vibes.
My process started with an OA that mixed formats. It began with multiple-choice questions, then shifted into LeetCode-style problems centered on ML algorithms. After I finished the coding portion, I moved into a DS/A interview that started with resume questions and then turned into a more structured technical discussion.
The online assessment typically includes multiple-choice questions, LeetCode-style coding problems, and machine learning tasks, often under tight time constraints. Candidates noted that the transition from basic questions to more complex ML coding tasks can be abrupt and challenging.
Technical interviews often cover a mix of SQL, Python coding, and machine learning concepts, with a focus on practical application and problem-solving. Candidates reported a variety of formats, including live coding and case studies, which can shift rapidly between topics.
Behavioral interviews are typically structured around candidates' past experiences and collaboration skills, with a focus on storytelling rather than just technical skills. Candidates felt that these rounds often emphasized cultural fit and teamwork.
Candidates frequently described the overall difficulty of the interview process as high, with significant pressure during both the OA and technical interviews. Many felt that the pacing and volume of questions could lead to a sense of being overwhelmed.
Experiences with recruiter communication varied, with some candidates noting delays that affected their overall experience. A clear and responsive communication style was appreciated, while any lack of clarity could add to the stress of the process.
Many candidates reflected on the importance of practicing under timed conditions and familiarizing themselves with both coding and ML concepts to better handle the pressure of the assessments. Some wished they had focused more on the practical application of ML algorithms.