Director, Data Science - AI Product & Risk| Minnetonka, Minnesota | Remote

Director, Data Science - AI Product & Risk

At United Healthcare, we're simplifying the health care experience, creating healthier communities and removing barriers to quality care. The work you do here impacts the lives of millions of people for the better. Come build the health care system of tomorrow, making it more responsive, affordable and optimized. Ready to make a difference? Join us to start Caring. Connecting. Growing together.

Build the AI playbook-and ship high-impact products safely. You'll translate business strategy into a focused AI portfolio, raise the bar on evaluation and production readiness, and put pragmatic governance in place so teams can move fast while managing model, data, security, compliance, and reputational risk.

AI is moving quickly-and scaling it in a real product environment requires more than great prototypes. This leader will bring alignment, standards, and operational excellence to ensure our AI experiences are measurable, reliable, and responsibly built - this requires leadership across many functional teams with in the company. You will be accountable for hiring and retaining a team of highly skilled professionals to deliver on the strategy.

The ideal candidate is someone who has a Data Science background and wants to move into a Product Organization to help drive results.

You'll own how AI products are delivered with an eye on high-quality and managing AI Risk
-This means setting the strategy, prioritization, delivery, measurement, and responsible operations-and serve as the escalation point when AI risk or incidents emerge.

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.

• Set the decision framework for risk:
  In partnership with Risk Teams across the enterprise, you will implement a tiered risk model that drives required reviews, test rigor, launch gates, and monitoring based on user impact and regulatory exposure
• Be a Product Leader of Responsible AI governance:
  Define standards for data use, documentation, evaluation, human oversight, accessibility, and customer transparency-aligned with RAI, Legal, Privacy, Security, Compliance, and Brand
• Raise the bar on delivery (MLOps/LLMOps):
  Standardize the path from dev to prod-versioning, reproducibility, CI/CD, model/prompt registries, evaluation harnesses, and rollback strategies
• Ensure Shipping with safeguards:
  Partner in ensuring red-teaming, bias/fairness checks, privacy reviews, security testing (e.g., prompt injection), and guardrails for customer-facing experiences occur
• Own monitoring & incident response:
  Define telemetry, alerts, and SLOs; build AI runbooks for drift, data/pipeline failures, vendor outages, harmful outputs, and policy changes; lead post-incident reviews and corrective actions
• Prove value with measurement:
  Define KPIs, experiment design, and success metrics for every initiative-tied directly to business outcomes
• Enable teams to move fast:
  Provide reusable patterns, templates, tooling guidance, and training so teams don't reinvent governance or delivery practices
• Lead and grow the team:
  Attract, mentor, and develop data scientists/ML engineers; set expectations and clear career pathways

• Proven experience leading data science/ML teams and delivering production AI in a product environment
• Solid understanding of modern ML and generative AI, including evaluation, monitoring, and lifecycle management
• Hands-on experience implementing Responsible AI practices (risk assessments, documentation, governance, audit readiness)
• Track record partnering with Product and Engineering to set strategy and deliver measurable outcomes
• Operational excellence: incident response leadership, reliability/SLO mindset, and crisp communication under pressure
• Ability to influence cross-functionally and drive alignment in ambiguous, fast-moving environments. The ability to articulate complex scientific concepts/language in a way that all stakeholders, partners, etc. can understand

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:

• 5+ years hands-on experience with NLP and/or LLM data pipelines, training datasets, annotation frameworks, and data quality diagnostics specific to chat/voice systems; proven ability to trace data issues to model behavior impacts
• 5+ years proven track record of experience setting data strategies that support product teams and making trade-offs between safety investment and product velocity; translates regulatory/risk requirements into actionable roadmaps
• 3+ years leading response to or learned from a safety incident, bias discovery, or data breach; demonstrates systems thinking…