Director, Data Science - Hybrid in MN or DC or Remote

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. , 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 United Health 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.

Primary 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

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

Required Qualifications:

* 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, Num Py, 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…