Solid ML/statistics fundamentals: supervised learning, evaluation methodology, feature engineering, bias/variance tradeoffs; deep learning or gradient boosting experience
Responsibilities
Ship production ML systems end-to-end: problem framing, data discovery, feature engineering, training, evaluation, deployment, monitoring, and iteration
Design robust ML system architectures with low-latency inference and high availability
Build and maintain reliable data and model pipelines using modern MLOps practices (CI/CD for ML, model registries, experiment tracking, automated retraining)
Contribute to technical scoping and break down complex initiatives into executable roadmaps; drive execution across cross-functional partners
Establish evaluation strategies: metrics, simulation, counterfactuals, and A/B tests; quantify impact and ensure statistical rigor
Implement model observability and governance: drift detection, performance monitoring, fairness/bias assessments, and model documentation
Collaborate closely with product, design, data, and platform teams to translate product goals into ML opportunities and measurable outcomes
Other
BS/MS/PhD in Computer Science, Engineering, Statistics, or related field, or 4+ years of equivalent practical experience
Excellent communication and product sense; able to translate business needs into technical plans and explain tradeoffs to non-ML stakeholders
All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy
Candidates are required to pass a drug test before beginning employment
UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status
