Director, Data Science - Hybrid in MN or DC or Remote
Role details
Job location
Tech stack
Job description
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.
Requirements
Do you have experience in SQL?, Do you have a Bachelor's degree?, * Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field
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12+ years of experience in data science, machine learning, or advanced analytics with 8+ years developing and deploying production ML models
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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
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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
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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
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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
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3+ years of experience designing, building, or operationalizing Generative AI or LLM-based systems
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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, * Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative discipline
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Experience defining or operationalizing enterprise MLOps, LLMOps, agent platform, or AI governance strategies
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Experience working with cloud-based data and analytics ecosystems such as Spark, Databricks, AWS, Azure, or GCP
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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
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Demonstrated external technical contributions such as publications, patents, or conference presentations in applied machine learning, healthcare analytics, or Generative AI
All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Benefits & conditions
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. 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. The salary for this role will range from $134,600 to $230,800 annually based on full-time employment. We comply with all minimum wage laws as applicable.