Data Science _Mulligan

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.
• Strong 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).