Data Scientist - Operational Optimization & Workforce Strategy

UnitedHealth Group•Eden Prairie, MN

23h•Remote

About The Position

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. We are seeking a highly skilled Data Scientist to lead advanced analytics and optimization initiatives supporting large-scale operational and workforce management challenges. This role will play a critical part in translating complex business problems into scalable data products, with a solid emphasis on enabling and accelerating our Palantir-driven ecosystem. You will operate at the intersection of data science, operations research, and platform-based analytics—designing solutions that not only generate insights but are embedded directly into decision-making workflows. This role requires both technical depth and the ability to influence cross-functional stakeholders across operations, product, and engineering. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Requirements

4+ years of experience in data science, applied analytics, or operations research
Experience working with Palantir Foundry or similar data platforms (Databricks, Snowflake + orchestration layers, etc.)
Demonstrated proficiency in Python and SQL (R acceptable but Python preferred)
Demonstrated experience building and deploying models in production environments
Hands-on experience with operational optimization, workforce planning, or supply/demand systems
Demonstrated foundation in: Statistics and machine learning
Demonstrated foundation in: Optimization techniques (LP, MIP, etc.)
Proven ability to work with large, complex datasets and translate insights into business impact
Proven communication skills with experience working cross-functionally

Nice To Haves

Background in operations research, industrial engineering, or service operations
Experience with optimization tools such as Pyomo, OR-Tools, Gurobi, or CPLEX
Experience building end-to-end data products, not just models
Familiarity with workforce management systems (e.g., NICE, Verint, Genesys)
Experience with experimentation frameworks and causal inference
Experience in healthcare operations, call center optimization, or large-scale service environments
Experience working in matrixed enterprise environments

Responsibilities

Design, develop, and deploy statistical, machine learning, and optimization models to improve operational efficiency and workforce performance
Build and enhance workforce management solutions including: Demand forecasting (time series, causal models), Capacity planning and scenario modeling, Scheduling and resource allocation optimization
Apply advanced optimization techniques (e.g., linear/integer programming, constraint optimization, simulation, heuristics)
Develop scalable analytical solutions within Palantir Foundry, including: Data pipelines and transformation logic, Operational data models / ontologies, Decision-support applications and workflows
Partner with engineering and platform teams to productionize models into reusable data products
Ensure solutions are maintainable, interpretable, and embedded into business processes
Translate ambiguous business problems into structured analytical frameworks
Define and operationalize KPIs, metrics, and success criteria tied to business outcomes
Identify inefficiencies and opportunities through deep analysis of large, complex datasets
Drive measurable impact through experimentation, A/B testing, and continuous model monitoring
Partner closely with operations, workforce management, and product leaders to align solutions with business priorities
Communicate complex analytical concepts and results to non-technical audiences
Influence decision-making through data storytelling and actionable recommendations
Build and maintain scalable data pipelines and analytical workflows
Develop dashboards and monitoring tools to support ongoing operations
Ensure data quality, governance, and model explainability