Real, anonymous reports from people who interviewed for Data Scientist at Trexquant Investment, newest first and distilled into what to expect across the loop.
My interview journey started with HR reaching out, and then I went through an online assessment that combined ML and DSA-style material. After that, I had a technical question round where they used 1–2 LeetCode problems in the medium-to-hard range. Even though I expected more direct data science or machine learning questioning, that never really came up beyond the assessment.
After applying, I had to get through a hangman-style assessment where the bar was at least 50% accuracy, with about a week to work on it. When I passed that, I moved into interviews that were more focused on Python, LeetCode-style coding, and general background questions like what I was doing in my current role and details around my situation. What stood out was that the hangman challenge came up again, and I had to redo it with a different approach, this time more explicitly tied to how I was thinking through the problem.
The interview process typically begins with an HR screening call that covers background and fit questions, followed by a technical interview that often emphasizes resume-driven discussions and coding skills. This phase sets the tone for the subsequent technical evaluations.
Candidates can expect a technical interview that includes live coding challenges, often based on LeetCode medium problems, with a focus on analytical thinking and problem-solving rather than traditional data science topics. This technical assessment is a key component throughout the process.
Interviews often emphasize quantitative reasoning, probability, and statistical concepts, with specific questions related to modeling and simulations, such as Markov chains or portfolio optimization. This focus distinguishes the role from typical data science interviews.
The onsite phase typically involves multiple back-to-back interviews with various team members, where candidates are drilled on their resume projects and technical skills, maintaining a consistent focus on analytical depth and communication skills throughout.
Some candidates may encounter take-home projects or coding challenges, such as a hangman-style assignment, which serve as initial filters before live interviews. Success in these tasks is crucial, as they heavily influence progression in the interview process.
Candidates often report a lack of clear feedback following interviews, which can lead to frustration, especially when the difficulty of the assessments does not align with the outcomes. Many candidates feel that the evaluation criteria are not always transparent.