Director, Data Science - Hybrid in MN or DC or Remote
About The Position
Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care’s most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together. Optum is a global organization delivering care, powered by data and technology, to help millions of people live healthier lives. At Optum.ai, we are not just witnessing the AI transformation in healthcare—we are leading it. Our mission is clear: to simplify healthcare with AI, turning insight into action at a scale few organizations in the world can match. As part of the Optum.ai team, you’ll work at the intersection of cutting-edge artificial intelligence and real-world healthcare impact. From reducing administrative burden for providers to anticipating patient needs and improving access to quality care, your work will help solve some of healthcare’s most complex challenges—and directly improve health outcomes for millions of people. You’ll collaborate with world-class talent across data science, engineering, product, and healthcare domains, backed by the reach and stability of Optum and UnitedHealth Group. Here, responsible innovation matters. So do comprehensive benefits, meaningful career growth, and the opportunity to make a tangible difference—advancing health equity and creating a simpler, more connected healthcare experience for everyone. This is more than a job. It’s a chance to shape the future of healthcare through the transformative power of AI. 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
- Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field
- 12+ years of experience in data science, machine learning, or advanced analytics with 8+ years developing and deploying production ML models
- 8+ years of experience using Python-based data science ecosystems (for example Pandas, NumPy, scikit-learn, PyTorch, or equivalent) and advanced SQL for large-scale analytics, experimentation, and data transformation
- 7+ years of experience in senior data science or technical leadership roles influencing modeling approaches, reviewing analytical work across teams, setting standards for model development and validation, and translating complex technical tradeoffs for senior stakeholders
- 6+ years of experience designing, deploying, or supporting production ML systems, including model serving, monitoring, retraining workflows, experimentation frameworks, ML lifecycle management, and evaluation of LLM or GenAI applications
- 6+ years of experience working with healthcare data such as claims, EHR, pharmacy, or laboratory datasets, including familiarity with healthcare coding systems such as ICD, CPT, NDC, SNOMED, and LOINC, as well as data interoperability standards including FHIR or HL7
- 3+ years of experience designing, building, or operationalizing Generative AI or LLM-based systems
- 1+ years of experience with Agentic AI concepts and implementations such as AI agents, agentic skills, model context protocols (MCPs), agent-to-agent (A2A) patterns, tool use, orchestration frameworks, or autonomous workflow execution
Nice To Haves
- Master’s or PhD in Computer Science, Statistics, Mathematics, or a related quantitative discipline
- Experience defining or operationalizing enterprise MLOps, LLMOps, agent platform, or AI governance strategies
- Experience working with cloud-based data and analytics ecosystems such as Spark, Databricks, AWS, Azure, or GCP
- Experience evaluating and implementing GenAI and agentic AI patterns such as retrieval-augmented generation, tool-calling, workflow automation, multi-agent collaboration, and safety guardrails in regulated environments
- Demonstrated external technical contributions such as publications, patents, or conference presentations in applied machine learning, healthcare analytics, or Generative AI
Responsibilities
- Define enterprise data science strategy
- Own and drive the technical strategy for applied machine learning, Generative AI, Agentic AI, and advanced analytics across multiple domains and healthcare use cases
- Lead development of advanced ML, GenAI, and agentic solutions
- Provide hands-on technical direction for the design, development, and deployment of machine learning, deep learning, time-series, survival analysis, large language model (LLM), and agent-based AI systems in production environments
- Establish modeling standards and best practices
- Define and standardize modeling frameworks, feature engineering approaches, prompt and context engineering practices, evaluation methodologies, and validation standards across data science teams
- Architect scalable ML and GenAI systems
- Guide the design of production-grade ML and LLM systems including data pipelines, feature stores, retrieval-augmented generation (RAG), model serving infrastructure, agent orchestration frameworks, monitoring, and retraining workflows
- Ensure responsible and reliable AI deployment
- Implement consistent practices for model interpretability, explainability, bias assessment, fairness evaluation, guardrails, human oversight, and lifecycle management across deployed predictive, generative, and agentic AI systems
- Oversee experimentation and performance monitoring
- Define experimentation, benchmarking, and monitoring strategies including drift detection, recalibration, LLM evaluation, hallucination and safety checks, tool-use reliability, and performance management
- Provide technical leadership and mentorship
- Mentor principal and senior data scientists, review technical designs and modeling decisions, and provide guidance for complex analytical, GenAI, and agentic AI challenges
- Influence cross-functional AI delivery
- Partner with engineering, data, security, product, and platform teams to align data science solutions with enterprise platforms, infrastructure, reliability requirements, AI governance expectations, and executive priorities
Benefits
- In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements).
- No matter where or when you begin a career with us, you’ll find a far-reaching choice of benefits and incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Director
