The process begins with screening potential candidates, followed by testing their ML depth to assess technical expertise. Next comes a case study, leading into a structured case study discussion. This transitions into a specific ML case study evaluation, and finally, assessing overall fitment for the role and organization.

1. Exploratory call: experience, interests 2. Take home assignment - jupyter notebook submission, classification problem (EDA, ft. engg, modelling etc.) 3. Follow up round on the assignment + breadth and depth of ML 4. Another technical round: ML concepts, case study question (e.g. ML logic behind insta stories) 5. Product sense interview with a PM 6. On site interview with hiring manager: technical theory questions + case study (problem solving) 7. Culture fitment interview with an HR 8. Tie breaker interview if any of the previous feedback is not positive