Principal Data Scientist

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.  

As Principal Data Scientist, this leader will manage and guide a team (or significant workstream) within the Payment Integrity Decision Intelligence (PIDI) Data Science and AIML Analytics India organization. The role focuses on executing data science and AI-led initiatives, delivering analytical excellence, and generating measurable business value across clinical and non-clinical payment integrity programs through a high-performing team of data scientists and AIML professionals.

The leader will foster a culture of innovation, scientific rigor, and continuous improvement while implementing best practices for model development, validation, monitoring, and performance optimization. They will collaborate closely with the Director and global analytics and data science teams.

Responsible for advancing PIDI analytical capabilities by leading the development, refinement, and deployment of predictive and prescriptive models that uncover actionable opportunities and drive measurable business outcomes, aligned with the broader team strategy.

Primary Responsibilities:

• Lead and manage a team of Data Scientists and AIML experts (or a major portfolio of initiatives), providing day-to-day direction, mentoring, coaching, and performance management to deliver AI, Machine Learning, and Advanced Analytics solutions that support payment integrity outcomes and business value
• Contribute to team vision, priorities, and execution roadmaps in partnership with the Director; translate strategic objectives into actionable plans and ensure on-time, high-quality delivery of scalable analytical solutions
• Partner with business, operations, clinical, product, and technology stakeholders to identify high-value opportunities, influence solution design, and drive adoption of data science and AIML outputs across clinical and non-clinical value streams
• Drive and contribute hands-on to the end-to-end AIML lifecycle for assigned areas, including problem formulation, data exploration, feature engineering, model development, validation, deployment, monitoring, and continuous improvement. Focus areas include detection of Fraud, Waste, Abuse and Error (FWAE), outliers or aberrant billing patterns, overpayments, gaps, and leakage opportunities
• Implement and strengthen model performance, governance, and value-realization practices within the team's scope, including success metrics, monitoring strategies, financial impact assessment, model validation, and optimization to maximize sustainable savings and outcomes
• Champion and apply advanced analytics and AI methodologies-including machine learning, deep learning, predictive and prescriptive analytics, anomaly detection, simulation, and statistical modeling-to solve complex healthcare and payment integrity challenges
• Support applied research, innovation, and modernization efforts by evaluating emerging fraud and abuse patterns, next-generation AI techniques, and analytical approaches, and by helping translate experimentation into production-ready solutions that improve detection accuracy and operational efficiency
• Uphold and operationalize Responsible AI practices, including data compliance, AIRB processes, model risk management, explainability, guardrails, and human-in-the-loop oversight, ensuring safe, ethical, and business-aligned deployment of AIML solutions
• Stay current with advancements in Data Science, AIML, Healthcare Analytics, and Payment Integrity, and actively share knowledge to elevate team capabilities and contribute to organizational innovation
• Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements.

Required Qualifications:

• Graduate (Master's/PhD preferred) degree in Engineering, Mathematics, Statistics, Computer Science, Artificial Intelligence, or a related quantitative discipline
• 12+ years of experience in Data Science, Advanced Analytics, AIML, and predictive modeling, with at least 6-8+ years leading teams, projects, or significant workstreams (people leadership experience strongly preferred)
• 8+ years of hands-on programming experience using Python, R, SAS, SQL, and distributed computing frameworks, including statistical and research-oriented analysis of diverse data types to build or optimize predictive methods
• Solid hands-on experience applying machine learning in healthcare or other high-stakes domains (direct experience in a cloud environment is highly valued)
• Solid expertise in Machine Learning, Statistical Modeling, Deep Learning, Natural Language Processing (NLP), and familiarity with Generative AI technologies
• Experience designing and delivering AIML solutions from concept through production deployment and business adoption
• Experience with cloud-based AIML platforms such as Azure AI, Azure Databricks, or equivalent
• Working knowledge of MLOps, Model Governance, Responsible AI, Model Monitoring, AI Risk Management, and AIRB frameworks
• Demonstrated ability to partner with senior stakeholders and business leaders to translate complex problems into practical, scalable AI solutions
• Demonstrated solid communication, stakeholder management, and presentation skills with the ability to influence decisions and drive alignment

Preferred Qualifications:

• Experience with Generative AI, Large Language Models (LLMs), Agentic AI, Copilot, Retrieval-Augmented Generation (RAG), AI automation, and ML engineering tools/frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, or LangGraph
• Experience deploying machine learning models at scale with CI/CD, automated monitoring, observability, and model lifecycle management
• Experience supporting AI-driven product development, innovation programs, or research-to-production initiatives
• Exposure to AIML system design, solution architecture, enterprise integration patterns, APIs, and microservices-based deployments
• Knowledge of data platforms and big data technologies including Snowflake, Databricks, Spark, Synapse Analytics, Microsoft Fabric, or Hadoop ecosystems
• Familiarity with Lean Six Sigma (Green Belt/Kaizen) or similar continuous improvement methodologies

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.