Required Skills & Experience

- Strong foundation in statistics and AI, including familiarity with the latest AI tools and frameworks (LLM-based extraction, GenAI/agentic workflows).

- Hands-on data science experience with feature engineering from raw/unstructured data (not just pre-aggregated variables).

- Advanced modelling capability: classical ML (XGBoost, Random Forest, etc.), sequence/trajectory methods (e.g., PrefixSpan, HDBSCAN, HMMs), survival/hazard modelling, and Bayesian statistical modelling.

- Demonstrated ability to move analytical work from insight to production-grade deliverable (APIs, libraries, or service wrappers not just notebooks).

- Proficiency in Python, with working experience in Git-based version control and CI/CD pipelines.

- Comfort working in regulated, sensitive-data environments with strict data handling and no-retention constraints.

- Robust written and verbal communication skills for a distributed offshore/onsite team model.

Nice to Have

- Prior experience in financial services / lending, credit bureau, or transaction-screening data.

- Experience building agentic evaluation/observability frameworks for AI systems (drift detection, hallucination checks).

📌 Data Science _Mulligan (Bengaluru)
🏢 Affine Analytics
📍 Bengaluru