Director, Data Science - AI Product & Risk

UnitedHealth Group•Minnetonka, MN

1d•Remote

About The Position

At UnitedHealthcare, 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. We’re looking for a Director, Data Science - AI Product & Risk to set the direction and operating rhythm for AI across Enterprise Digital Product. 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.

Requirements

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
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 about root causes; shows genuine commitment to transparency and fixing underlying processes
Fluent in Security, Privacy, Legal, and AI Ethics; proven ability to bridge conflicting requirements across multiple risk functions and build durable governance models that multiple teams trust and enforce
Proven ability to influence VPs and C-suite on complex decisions; comfortable presenting data/AI risk trade-offs to boards; demonstrates healthy disagreement and consensus-building across competing interests
Led geographically distributed or matrixed teams across data, AI, and business functions; experience convening risk/policy stakeholders and making trade-off decisions that multiple functions can live with
Successfully hiring, developing, and leading senior data and product professionals; provides technical direction while enabling autonomy; creates psychological safety for raising hard concerns
Demonstrated track record building or scaling safety/governance frameworks for AI products; deep knowledge of bias, toxicity, hallucination, and jailbreak vectors; experience implementing measurable safety controls and audit mechanisms
Legally authorized to work in the US without any restrictions. Proof will be required.
All employees working remotely will be required to adhere to UnitedHealth Group’s Telecommuter Policy

Responsibilities

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