Sr Data Scientist - AI or ML Engineering Focus - Remote

UnitedHealth Group•Minnetonka, MN

19h•$91,700 - $163,700•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 A Senior Data Scientist (Grade 28) specializing in AI/ML engineering for EAR projects impacting the UHC COVE. This role is a primary hands-on contributor for building and deploying machine learning models. It emphasizes practical engineering of AI solutions: rigorous model development, evaluation of modeling approaches, and implementing MLOps pipelines to ensure models are effectively integrated and maintained in production. 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

Bachelor's Degree in Computer Science, Data Science, or related field
5+ years of experience in data science, machine learning, or related roles. Solid foundation in statistical modeling and machine learning techniques. Hands-on experience developing models for real-world problems and improving them based on feedback and data
Demonstrated experience deploying and maintaining ML models in a production environment. Comfort with the end-to-end MLOps lifecycle: using source control, CI/CD pipelines, and orchestration to automate model deployment. Should understand concepts like model versioning, reproducibility, and monitoring in production
Proficiency in Python and PySpark for building ML models and automating tasks. Solid SQL skills for data extraction and manipulation
Proven ability to work independently on complex technical problems. Solid troubleshooting skills to debug issues whether they stem from data quality, model behavior, or pipeline failures. A mindset geared towards automation and efficiency, always looking for ways to streamline repetitive tasks

Nice To Haves

Experience with specific MLOps and cloud tools (e.g., Databricks MLflow for experiment tracking and model registry, GitHub Actions for CI). Familiarity with infrastructure-as-code for deploying ML infrastructure
Experience in mentoring junior data scientists or leading technical workstreams will be beneficial. The ability to document work clearly and impart knowledge to others helps the overall team
Familiarity with the Databricks Lakehouse platform and Spark. For example, knowing how to implement ML pipelines on Databricks, use Delta Lake for data versioning, and optimize Spark jobs for feature processing
Exposure to real-time or streaming data analysis. Experience deploying models that consume streaming data (e.g., streaming analytics or real-time dashboards) or working with technologies like Kafka for live data feeds

Responsibilities

Develop machine learning models tailored to project requirements (classification, regression, forecasting, etc.). Perform thorough evaluations and experiments to compare model performance and iterate to optimize results. Ensure that chosen models are robust and generalize well
Take ownership of deploying models into production. Build and maintain automated pipelines for model serving, including steps for data preprocessing, model inference, and continuous monitoring. Implement version control for models and manage a schedule for retraining or refreshing models as data evolves
Integrate AI/ML solutions with the broader application ecosystem. Work closely with data engineers to ensure pipelines provide the needed data to models in the correct format and frequency (batch or streaming). Collaborate with software engineers to embed model outputs into user-facing applications or workflows
Stay up to date with new tools and techniques in AI engineering. Evaluate external solutions or libraries that could accelerate development (e.g., AutoML tools, specialized model frameworks) and make recommendations. Build proof-of-concept demonstrations to assess new approaches or technologies for the project
Implement monitoring and alerting for model performance and data drift. Troubleshoot issues in model predictions or system integration quickly. Optimize model runtime and resource usage (e.g., improving inference speed or reducing memory footprint) to meet production SLAs