> Markdown version of [/jobs/ext/3730529-data-scientist-indianapolis-health](https://www.wearedevelopers.com/jobs/ext/3730529-data-scientist-indianapolis-health). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - Indianapolis Health - **Company:** Milliman, Inc. - **Location:** Indianapolis, IN, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** LTE (Telecommunication), Artificial Intelligence, Automation of Tests, Network Analysis, Cloud Database, Code Review, Information Engineering, DevOps, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, Modular Design, Natural Language Processing, Object-Oriented Software Development, Pattern Recognition, Standard Sql, Data Logging, Feature Engineering, Delivery Pipeline, Apache Spark, Generative AI, Containerization, AI Platforms, Pyspark, Information Technology, Performance Monitor, Machine Learning Operations, Drift Detection, Automation Anywhere, Docker, Databricks - **Published:** October 14, 2026 - **Apply:** https://diversityjobs.com/career/18628710/Data-Scientist-Indianapolis-Health-Indiana-Indianapolis ## About the Role * 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 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. * Client-Facing Delivery: 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. Required * 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. Preferred * 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. Individual(s) must be legally authorized to work in the United States without the need for immigration support or sponsorship from Milliman now or in the future., Masters (preferred) ## Description 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., The person hired for this role will work in a dynamic, hybrid environment, with 2 to 3 days per week of on-site work required in our Indianapolis office on a weekly basis. Compensation A combination of factors will be considered to determine the compensation for this role, including, but not limited to, education, relevant work experience, qualifications, skills, certifications, etc., Through a team of professionals ranging from actuaries to clinicians, technology specialists to plan administrators, we offer unparalleled expertise in employee benefits, investment consulting, healthcare, life insurance and financial services, and property and casualty insurance.