Data Scientist - Indianapolis Health

13-Indianapolis
Indianapolis, IN 46204, USA

Overview

Milliman's Indianapolis Health practice is seeking a highly skilled and motivated Data Scientist to join our growing practice. This role is focused on applied data science and analytics in healthcare, including the enhancement of existing analytical products and the development of new solutions to extend our AI and ML portfolio. The ideal candidate has hands-on experience with healthcare data and combines strong statistical and machine learning expertise with consulting skills to translate complex problems into practical, scalable solutions.

Successful candidates will contribute across the full lifecycle of data science initiatives, from problem definition and exploratory analysis through production deployment, client communication, and product enhancement.

Responsibilities

In this role, you will:

• Product Development & Innovation: Enhance and extend existing AI/analytics products while contributing new concepts, methods, and prototypes that address complex healthcare challenges.
• Model Development & Interpretability: Develop interpretable, defensible ML/analytical models and statistical methods to support user workflows, including explainable feature attribution, comparative benchmarking, and structured model output summaries.
• Client Deliverables & Communication: Produce high-quality written reports, exhibits, and presentations that clearly communicate methods, findings, limitations, and recommended actions to non-technical audiences.
• Advanced Analytics & Emerging Technologies: Evaluate and apply modern analytical techniques, including machine learning, generative AI, natural language processing, and other emerging approaches when appropriate.
• Operational ML Excellence: Own model performance by defining acceptance metrics, monitoring data health (including drift), tuning thresholds, and designing dashboards for triage and KPI tracking.
• Business Development Support: Support proposals and RFPs by drafting technical approach sections, methods descriptions, solution diagrams, and participating in capability demos.
• Cross-Functional Collaboration: Partner with domain SMEs (actuarial, clinical, pharmacy, policy) to translate requirements into quantifiable solutions, validate outcomes, and align outputs to real operational decisions.
• Engineering Best Practices: Collaborate with data engineering to build scalable pipelines with robust quality controls, reproducibility, logging, and documentation.
• Product Stewardship: Contribute to the long-term evolution of analytical products and platforms by identifying enhancement opportunities, gathering user feedback, prioritizing improvements, and balancing innovation with maintainability and reliability.

Qualifications

• Consulting-Grade Communication: Demonstrated ability to write clear client-ready reports, build presentations, and explain limitations and tradeoffs to non-technical stakeholders.
• Applied ML & Statistical Rigor: Strong analytical skills spanning statistical modeling, machine learning, predictive analytics, anomaly detection, and quantitative problem solving, with the ability to explain methodologies and limitations clearly.
• End-to-End ML Delivery: Proven experience taking projects from ambiguous problem framing to maintainable deployment and adoption in operational workflows.
• Healthcare Data Expertise: Hands-on experience with healthcare data (claims/encounters preferred), including feature engineering, validation, and explainability for audit/oversight workflows.
• Applied Data Science
  
  Experience:
  
  Experience applying modern AI or machine learning techniques to real-world business problems, including evaluation, validation, and deployment considerations.
• Production Engineering: Strong Python…