The interview process was conducted in the form of a hackathon-based evaluation, designed to test both technical skills and real-world problem-solving ability.
Problem Statement: Candidates were given a real-world problem to solve within a fixed time.
End-to-End Execution: The task involved understanding the problem, data preprocessing, feature engineering, model selection, training, and evaluation.
Hands-on ML Focus: Emphasis was on practical machine learning skills rather than theoretical questions.
Code & Approach Review: Interviewers evaluated code quality, logic, choice of algorithms, and overall approach.
Explanation & Justification: Candidates were asked to explain their decisions, assumptions, and results.
Outcome-Oriented: Performance was judged on correctness, clarity, efficiency, and the ability to deliver a working solution under time constraints.
Overall, the hackathon acted as a practical interview, assessing problem-solving, ML fundamentals, and execution skills in a real development scenario.