Solicité el puesto a través de un captador. El proceso duró 4 semanas. Acudí a una entrevista en Navi (Bengaluru) en jun 2023
Entrevista
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
Preguntas de entrevista [1]
Pregunta 1
1. Why is PR auc better than ROC auc for imbalanced classification? 2. How is scaling done on train and test data 3. Case study of designing insta story recommender 4. What is expectation maximization 5. Business case of designing a washing machine for blind folks 6. How to measure model / data drift in real setting
Solicité el puesto a través de un captador. El proceso duró 2 meses. Acudí a una entrevista en Navi (Bengaluru) en ago 2025
Entrevista
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.