Must Have Skills :

- Strong understanding of Statistics, Probability, Linear Algebra, and ML fundamentals

- Hands-on experience with classical Machine Learning algorithms: Regression, Classification, Clustering, Recommendation Systems, and Time Series Forecasting

- Experience in feature engineering, data preprocessing, and model evaluation

- Understanding of model performance metrics (AUC, NDCG, precision/recall, F1) and validation techniques

- Experience in developing and deploying ML models into production environments

- Understanding of model observability, logging, monitoring, and debugging

- Proficient in Python with ability to write clean, production-ready code under time constraints. Candidates will be tested on live coding during the interview process.

- Proficient in SQL: window functions, CTEs, conditional aggregation, and analytical queries. Candidates will be tested on live SQL during the interview process.

- Familiarity with data pipeline orchestration tools (Airflow, Prefect, or similar)

- Exposure to cloud platforms and basic MLOps concepts

- Basic understanding of data engineering concepts like ETL design, Medallion architecture, etc.

Good-to-Have :

- Experience with feature stores and online/offline feature serving

- Understanding of real-time model serving and latency-sensitive inference

- Experience with Databricks and Snowflake is a plus.

- Retail domain exposure

- Experience in Personalization and Recommendation use cases

What this role is NOT :

- This is not a GenAI/LLM application developer role. Candidates whose primary experience is only building chatbots, RAG pipelines, or calling LLM APIs without classical ML depth will not meet the bar.