The process started with a technical assessment lasting about an hour, covering machine learning fundamentals through multiple-choice questions on RAG, deep learning, and standard ML concepts. Then came the practical coding portion: I worked with two datasets, building a classifier using Naive Bayes and a regression model with Linear Regression and TF-IDF vectorization. Beyond the immediate technical rounds, there was also a case study component spanning two days where I had to demonstrate end-to-end model development. The case involved churn prediction, and I needed to articulate not just which models I chose and why, but also the broader architecture of my ML pipeline, data governance considerations, and how I'd structure the project in a real-world setting. Interviewers asked probing questions about model selection rationale and which data lake infrastructure I'd leverage. The interview experience mixed straightforward behavioral elements—discussing past projects, problem-solving approaches, and teamwork—with deeper technical dives into my approach to data collection, cleaning, and handling of missing or imbalanced data. I found the emphasis on end-to-end thinking and governance alongside pure model building was what set this apart from typical data science interviews. The process was thorough and felt designed to assess whether candidates could think beyond algorithms to implementation and organizational context.
Prep tip from this candidate
Prepare a detailed, end-to-end case study narrative—not just algorithms, but data governance, pipeline architecture, and production considerations. Be ready to justify model choices and discuss infrastructure trade-offs like data lake selection.
