Data Scientist

Responsibilities:
End-to-End Ownership: The role expects candidates to take ambiguous business/clinical questions, scope them into data problems, build models, and deliver measurable outcomes.
Core Work: Predictive modeling in supply chain forecasting, claims/denial analytics, and population health indicators.
Advanced ML and AI: Strong emphasis on NLP, LLMs, and RAG systems, including text-to-SQL, retrieval over schemas, and grounding outputs to trusted data.
Production Focus: Models must be production-ized pipelines, with retraining, monitoring, and drift detection, not just research prototypes.
Collaboration: Partnering with product, domain, and government stakeholders; clear communication of findings and limitations.
Leadership: Setting analytical standards, mentoring junior scientists, and contributing to architecture/tooling decisions.
Requirements:
Experience: 5+ years in data science with shipped models.
Tech Stack: Python (Pandas, NumPy, scikit-learn, and deep learning frameworks), SQL, and columnar DBs (ClickHouse, BigQuery, and Snowflake).
Specialized Skills: NLP, LLMs, RAG, text-to-SQL, MLOps.
Nice-to-Have: Health data (FHIR, claims, logistics), time-series forecasting, causal inference, LangChain/LlamaIndex, dbt, and lakehouse architectures.

Experience
5-9 yrs