Real, anonymous reports from people who interviewed for Data Scientist at Pinterest, newest first and distilled into what to expect across the loop.
The process started with an online assessment that ran for about 70 minutes. It opened with seven multiple-choice questions, which felt manageable at first, and then it shifted into a coding portion that was much more frustrating than the time limit suggested.
My interview journey was pretty compact: an online assessment first, then a follow-up round that mixed resume discussion with a DS/A-focused technical interview. The assessment itself combined multiple-choice questions with LeetCode-style problems centered on machine learning algorithms.
The online assessment typically consists of multiple-choice questions, machine learning concepts, and coding tasks, often escalating in difficulty and requiring strong foundational knowledge. Candidates should prepare for a mix of theoretical and practical questions, including LeetCode-style problems and implementation tasks.
Subsequent technical interviews often involve discussions around the candidate's resume, followed by coding challenges that may include data structures, algorithms, and practical data science applications like SQL and Python tasks. Expect a focus on both theoretical understanding and practical problem-solving skills.
Candidates can expect behavioral interviews that assess cultural fit and collaboration skills, often involving discussions about past experiences and teamwork. It's important to prepare for questions that explore how candidates work with others and handle challenges.
The overall difficulty of the interview process is perceived as high, with candidates noting that even small gaps in knowledge can impact outcomes. It's crucial to be well-prepared across all topics, as the evaluation is thorough and structured.
Candidates have reported issues with communication and scheduling, which can affect the overall experience. Timely responses from recruiters and clarity about the process are essential, so candidates should be proactive in following up.
Interviews often include practical case studies and experimental design questions, emphasizing the application of data science principles in real-world scenarios. Candidates should be prepared to discuss A/B testing and other analytical frameworks.
