AI Data Scientist – Indianapolis Health

Milliman•Indianapolis, IN

17h•$93,700 - $154,500•Hybrid

About The Position

Milliman’s Indianapolis Health practice is seeking a highly skilled and motivated AI Data Scientist to join our growing practice. This role is focused on applied machine learning for healthcare, enhancing and extending an existing production AI and analytics platform, and contributing new ideas and prototypes across our broader artificial intelligence (AI) and machine learning (ML) portfolio. The ideal candidate has hands-on experience with healthcare data, strong ML and statistical fundamentals, and the ability to operationalize results through dashboards. You will also contribute to applied large language model (LLM) capabilities, including prompt design and agent-style workflow automation, using disciplined evaluation, traceability, and guardrails appropriate for regulated environments. In this role, you will support: Production AI & Prototyping: Enhance and extend existing production AI/analytics platforms and develop new applications through research, ideation, and rapid prototyping to solve complex public sector healthcare challenges. Model Development & Interpretability: Develop interpretable, defensible ML 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. Governed GenAI & LLMs: Engineer governed solutions including prompt design, RAG, and agentic workflow orchestration (e.g., MCP) with rigorous evaluation and traceability for regulated use. 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.

Requirements

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 applied ML skills on large-scale data with interpretable methods, comparative analytics, and defensible anomaly scoring approaches suitable for regulated review and support contexts.
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 Generative AI: Practical experience with prompt design, API-based integration, and governed retrieval (RAG) with evaluation and guardrails.
Production Engineering: Strong Python skills in shared codebases (OOP, modular design) with modern engineering discipline (testing, code reviews, structured logging, Git/GitHub).
Data at Scale: Strong SQL plus distributed processing (Spark, PySpark, Databricks preferred) with strong data quality and validation practices.
Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field
5+ years of experience in data science, machine learning, or AI engineering roles

Nice To Haves

Master’s degree in Data Science, Computer Science, Statistics, or a related field
Experience facilitating client workshops to define scope, success metrics, validation plans, and acceptance criteria, including documenting decisions and next steps
Experience translating model outputs into structured, explainable artifacts (drivers, supporting exhibits, summary narratives) that enable non-technical reviewers and stakeholders to act on results with confidence
Experience leading executive briefings and aligning stakeholders on findings, tradeoffs, and recommended actions in regulated environments
Experience supporting proposals or capability briefings with technical content, including methods descriptions, solution diagrams, and demo narratives
Experience with anomaly detection and risk stratification methods (peer grouping, statistical scoring, outlier identification) applied to large-scale operational datasets
Experience with MLOps and DevOps practices, including CI/CD pipelines, containerization (e.g., Docker), infrastructure-as-code, and automated testing and deployment workflows for AI/ML systems
Experience designing agentic AI workflows, including tool-use orchestration via protocols such as MCP, with appropriate guardrails, logging, and human-in-the-loop controls
Time series analysis applied to trend detection, seasonality, and behavioral pattern recognition in operational datasets
Graph or network analytics applied to relationship modeling, entity resolution, or pattern detection in complex relational datasets
Model monitoring and operational ML practices, including drift detection, retraining strategies, and automated evaluation in production
Experience with cloud-based data and AI platforms, including managed search or retrieval capabilities to support governed analytics and AI workflows
Experience improving retrieval quality for governed AI workflows (chunking strategies, relevance evaluation, grounding checks, and citation accuracy)
Experience building document ingestion and extraction pipelines that convert PDFs, Word, and Excel into structured, validated datasets suitable for downstream analytics

Responsibilities

Enhance and extend existing production AI/analytics platforms and develop new applications through research, ideation, and rapid prototyping to solve complex public sector healthcare challenges.
Develop interpretable, defensible ML models and statistical methods to support user workflows, including explainable feature attribution, comparative benchmarking, and structured model output summaries.
Produce high-quality written reports, exhibits, and presentations that clearly communicate methods, findings, limitations, and recommended actions to non-technical audiences.
Engineer governed solutions including prompt design, RAG, and agentic workflow orchestration (e.g., MCP) with rigorous evaluation and traceability for regulated use.
Own model performance by defining acceptance metrics, monitoring data health (including drift), tuning thresholds, and designing dashboards for triage and KPI tracking.
Support proposals and RFPs by drafting technical approach sections, methods descriptions, solution diagrams, and participating in capability demos.
Partner with domain SMEs (actuarial, clinical, pharmacy, policy) to translate requirements into quantifiable solutions, validate outcomes, and align outputs to real operational decisions.
Collaborate with data engineering to build scalable pipelines with robust quality controls, reproducibility, logging, and documentation.

Benefits

Medical, Dental and Vision - Coverage for employees, dependents, and domestic partners
Employee Assistance Program (EAP) - Confidential support for personal and work-related challenges
401(k) Plan - Includes a company matching program and profit-sharing contributions
Discretionary Bonus Program – Recognizing employee contributions
Flexible Spending Accounts (FSA) - Pre-tax savings for dependent care, transportation, and eligible medical expenses
Paid Time Off (PTO) – Begins accruing on the first day of work. Full-time employees accrue 15 days per year, and employees working less than full-time accrue PTO on a prorated basis.
Holidays - A minimum of 10 observed holidays per year
Family Building Benefits including Adoption and fertility assistance
Paid Parental Leave - Up to 12 weeks of paid leave for employees who meet eligibility criteria
Life Insurance & AD&D - 100% of premiums covered by Milliman
Short-Term and Long-Term Disability – Fully paid by Milliman