Derived from 1 submission for ALL. Format details may vary across different teams or locations.
Cars24’s Data Science / ML hiring process is designed to assess both technical depth and practical problem-solving skills. Candidates are expected to demonstrate expertise in machine learning, coding, and project execution, with a strong emphasis on clear communication and data-driven decision-making. The process includes a home assignment, two technical rounds, and an HR round, with feedback often influenced by project explanation quality and alignment with team expectations.
InterviewDigestarr = ['1','2',20,0.91, 0.77,'3','4']The candidate went through four rounds: a home assignment, two technical rounds, and an HR round. Despite solving all tasks, the candidate received negative feedback and was rejected, potentially due to hiring decisions, relocation costs, or performance in explaining a project feature.
Candidates were given 3-4 days to complete a machine learning use case and a coding question. The use case involved predicting engine ratings, and the coding question involved mapping flights to trips using a recursive approach.
A 45-minute interview focused on the candidate's home assignment solution. The interviewer asked detailed questions about the approach, model performance, metrics used, encountered issues, insights from the data, and potential improvements if more time was available. Basic ML questions about regularization (L1 and L2) were also asked.
A 60-minute interview where the candidate was asked to explain two projects from their resume. The interviewer provided feedback on one project's feature explanation. A coding question was given to sort an array while preserving data types. The candidate was expected to write working code rather than just explaining the approach.
Sort an array while preserving data types: arr = ['1','2',20,0.91, 0.77,'3','4']
A 30-minute HR round where basic questions were asked, and the candidate inquired about roles and responsibilities. The HR later informed the candidate of negative feedback and that they would not proceed further.