Data Scientist - Indianapolis Health

Milliman•Indianapolis, IN

5d•Hybrid

About The Position

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.

Requirements

Demonstrated ability to write clear client-ready reports, build presentations, and explain limitations and tradeoffs to non-technical stakeholders.
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.
Proven experience taking projects from ambiguous problem framing to maintainable deployment and adoption in operational workflows.
Hands-on experience with healthcare data (claims/encounters preferred), including feature engineering, validation, and explainability for audit/oversight workflows.
Experience applying modern AI or machine learning techniques to real-world business problems, including evaluation, validation, and deployment considerations.
Strong Python skills in shared codebases (OOP, modular design) with modern engineering discipline (testing, code reviews, structured logging, Git/GitHub).
Strong SQL plus distributed processing (Spark, PySpark, Databricks preferred) with strong data quality and validation practices.
Experience presenting analytical findings, facilitating discussions with stakeholders, and guiding decision-making through clear communication of results, assumptions, limitations, and recommendations.
Basic knowledge of Gen AI tools, how to utilize AI to enhance productivity, and potential ethical considerations or concerns.
Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field.
2-5+ years 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 evaluating and operationalizing emerging analytical technologies, including machine learning and generative AI solutions.
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.

Responsibilities

Enhance and extend existing AI/analytics products while contributing new concepts, methods, and prototypes that address complex healthcare challenges.
Develop interpretable, defensible ML/analytical 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.
Evaluate and apply modern analytical techniques, including machine learning, generative AI, natural language processing, and other emerging approaches when appropriate.
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.
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.

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 – 11 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