Own end-to-end solutions: translate ambiguous healthcare and operational problems into AI/ML models that deliver measurable impact by selecting methods, building data/models, deploying services, instrumenting observability, and real-world feedback loops
Partner with Product, Engineering, and Clinical leaders to embed data science into workflows like claims processing, utilization management, provider network optimization, risk adjustment, etc.
Deliver clinical intelligence features such as autonomous chart review, disease detection, compliance forecasting, and quality analytics
Build AI/NLP/LLM models for document understanding and information extraction, including OCR, NER, and vision models
Advance quality and payment integrity: create models and automated QA to reduce manual review, increase accuracy, and surface documentation anomalies and audit risk
Champion engineering excellence: internal libraries, reusable components, coding standards, code reviews, and clear documentation
Own the lifecycle: monitoring, drift detection, alerting, and post-deployment reviews.

2-5 years delivering production-grade data science or ML software with measurable impact
Strong software engineering fundamentals: data structures, algorithms, modular design, API design, documentation
Proficiency in Python plus SQL; experience building reusable packages, utilities, and CLI tools
Applied knowledge of ML (tree ensembles, boosting, NLP/LLMs, deep learning) and experimental design
Experience with high-volume, high-dimensional, and unstructured data; strong data quality mindset
Experience with Git workflows, code reviews, CI/CD
Excellent data visualization and storytelling skills
Ability to thrive in ambiguous, fast-paced environments
Preferred: Healthcare domain experience; familiarity with CMS/Medicare Advantage operations
Document understanding systems with OCR/NER and LLM pipelines
Experience with NoSQL and performance optimization
Contributions to internal frameworks or open-source; published work.

Data Scientist

Key skills