Job description

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 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. The Team The Indianapolis Health Practice has 250+ full-time employees from diverse academic and professional backgrounds. Our practice is structured to serve state Medicaid and other health and human service agencies through dedicated core teams tailored to each client’s unique needs. As a member of the Indianapolis Data Engineering, Architecture, and Science (IDEAS) team, you will provide advanced analytics and data science support to key state clients and their core teams under the mentorship of senior data consultants. Our team works cross-functionally with actuarial, policy, finance, pharmacy, clinical, and data management professionals to deliver practical, defensible results. You will work on challenging problems, collaborate with experienced colleagues, and contribute across a variety of client programs and solution areas with opportunities to learn, grow, and take on increasing ownership over time. Location 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. Who We Are Independent for over 75 years, Milliman delivers market-leading services and solutions to clients worldwide. Today, we are helping companies take on some of the world’s most critical and complex issues, including retirement funding and healthcare financing, risk management and regulatory compliance, data analytics, and business transformation. Milliman invests in skills training and career development and gives all employees access to a variety of learning and mentoring opportunities. Our growing number of Milliman Employee Resource Groups (ERG’s) are employee-led communities that influence policy decisions, develop future leaders, and amplify the voices of their constituents. We encourage our employees to give back to their varied professions, including leadership in professional organizations. Please visit our web site ( milliman.com ) to learn more about Milliman’s commitments to our people, inclusion, and sustainability. 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. Benefits We offer a comprehensive benefits package designed to support employees’ health, financial security, and well-being. Benefits include: • 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 Equal Opportunity All qualified applicants will receive consideration for employment, without regard to race, color, religion, sex, sexual orientation, national origin, disability, or status as a protected veteran. #LI-LB1 #LI-HYBRID #LI-ONSITE

Requirements

Must-haves

Product Development & Innovation: Enhance and extend existing AI/analytics products while contributing new concepts, methods, and prototypes that address complex healthcare challenges.
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.
Consulting-Grade Communication: Demonstrated ability to write clear client-ready reports, build presentations, and explain limitations and tradeoffs to non-technical stakeholders.
End-to-End ML Delivery: Proven experience taking projects from ambiguous problem framing to maintainable deployment and adoption in operational 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).
Basic knowledge of Gen AI tools, how to utilize AI to enhance productivity, and potential ethical considerations or concerns.

Nice-to-haves

Healthcare Data Expertise: Hands-on experience with healthcare data (claims/encounters preferred), including feature engineering, validation, and explainability for audit/oversight workflows.
Data at Scale: Strong SQL plus distributed processing (Spark, PySpark, Databricks preferred) with strong data quality and validation practices.
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.

Application prep

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Keywords to mirror

aimlmachine learningpythonsqlci/cddocker

Platform: ukg
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Paid Time Off

Parental Leave