Sr Data Scientist - AI or ML Engineering Focus - Remote

UnitedHealthcare is simplifying the health care experience, creating healthier communities, and removing barriers to quality care. The Senior Data Scientist will build, evaluate, deploy, and maintain machine learning models, develop MLOps pipelines, integrate AI/ML solutions with application ecosystems, and optimize production performance.

Responsibilities:

• Model Development & Evaluation: 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
• MLOps & Deployment: 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
• Solution Engineering: 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
• Technical Innovation: 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
• Operational Excellence: 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

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
• Understanding of 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
• Experience with specific MLOps and cloud tools (e.g., Databricks MLflow for experiment tracking and model registry, GitHub Actions for CI)
• Experience in mentoring junior data scientists or leading technical workstreams will be beneficial
• Familiarity with the Databricks Lakehouse platform and Spark
• 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