The interview process spanned 2 months and had 5 rounds, starting with 2700 participants, with only (5+3) final selections. Round 1: Conducted on the WeCP platform (150 mins). It included: Coding: 3 problems (e.g., Sieve of Eratosthenes, Maximum Sum Subarray). Debugging: 2 simple C++ questions. MCQs: 30 questions (verbal, reasoning, numerical). Round 2: Focused on ML concepts (coding and MCQs). Coding: Logistic regression and WCSS computation in Python. MCQs: Advanced and In-depth ML/DL topics. Round 3: Technical discussion involving project explanation, case studies (e.g., retail data analysis), and live data insights generation (under 15 mins). Round 4: Resume walkthrough, project deep dives, and ML evaluation metrics like recall vs precision. Round 5: HR round covering personal background, interests, and industry-relevant topics like Explainable AI (XAI).

The interview process spanned 2 months and had 5 rounds, starting with 2700 participants, with only (5+3) final selections. Round 1: Conducted on the WeCP platform (150 mins). It included: Coding: 3 problems (e.g., Sieve of Eratosthenes, Maximum Sum Subarray). Debugging: 2 simple C++ questions. MCQs: 30 questions (verbal, reasoning, numerical). Round 2: Focused on ML concepts (coding and MCQs). Coding: Logistic regression and WCSS computation in Python. MCQs: Advanced and In-depth ML/DL topics. Round 3: Technical discussion involving project explanation, case studies (e.g., retail data analysis), and live data insights generation (under 15 mins). Round 4: Resume walkthrough, project deep dives, and ML evaluation metrics like recall vs precision. Round 5: HR round covering personal background, interests, and industry-relevant topics like Explainable AI (XAI).