Real, anonymous reports from people who interviewed for Data Scientist at Turing, newest first and distilled into what to expect across the loop.
After a recruiter touchpoint, I went through three rounds that felt pretty tightly focused on machine learning. There was also an online assessment that I found genuinely tough, and the questions across the process skewed toward ML algorithms rather than broad generalist topics.
My process moved through two technical interviews and then a discussion. I started with an assessment-style round, then about a week later I had a face-to-face technical interview that lasted around an hour. The final step was a discussion round, which felt more conversation-based after all the technical content.
The interview process typically consists of multiple rounds, starting with an online assessment followed by technical interviews and concluding with an HR discussion. Candidates noted that the scheduling was generally clear and organized, reducing stress around logistics.
Candidates faced a mix of coding challenges, including LeetCode-style problems, data analysis tasks, and machine learning concepts, with a strong emphasis on Python and SQL. The technical interviews were designed to evaluate both algorithmic thinking and practical application under time constraints.
A significant portion of the interviews concentrated on machine learning algorithms and their practical applications, with candidates expected to demonstrate a deep understanding of concepts and reasoning behind various ML techniques.
The HR rounds primarily focused on logistical questions and candidate motivations, with some candidates noting a lack of detailed feedback post-interview. This part of the process felt less rigorous compared to the technical assessments.
Candidates reported a high-pressure environment during technical assessments, with challenging questions designed to test problem-solving abilities under time constraints. Many felt that the intensity of the interviews reflected the company's high expectations.
Some candidates expressed a desire to better prepare for the specific technical topics covered, particularly in machine learning and Python intricacies, as well as to seek more feedback during the process to improve their chances in future interviews.
