Data Scientist 2

UnitedHealth Group•Eden Prairie, MN

19h•Remote

About The Position

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. You will enjoy the flexibility to telecommute from anywhere within the U.S. as you take on some tough challenges. Primary Responsibilities: Participate in the design, development, and deployment of applied machine learning solutions addressing complex business and clinical problems using large-scale healthcare data Drive the end-to-end model lifecycle: problem framing, feature engineering, model development, evaluation, validation, explainability, deployment, and post-production monitoring Develop and review production-grade Python code following software engineering best practices (testing, modularization, version control, CI/CD) Support architecting scalable data science workflows using Python, SQL, and distributed data processing frameworks in cloud or enterprise environments Apply and advance classical ML, deep learning, time-series modeling, and survival analysis techniques based on business needs Ensure models are interpretable, explainable, and compliant with enterprise governance, regulatory, and ethical standards (e.g., bias, fairness, auditability) Partner with engineering, product, clinical, and business stakeholders to translate ambiguous problems into actionable analytical solutions Review and approve modeling approaches, assumptions, and results; influence architectural and methodological decisions across teams Communicate insights, risks, and tradeoffs clearly to technical and executive audiences Stay current with emerging methods in applied ML, healthcare analytics, and MLOps, and drive adoption of best practices 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

4+ years of experience building production-quality, maintainable, and testable code
4+ years of experience with machine learning and statistical modeling fundamentals, including:
Feature engineering and selection
Model training, tuning, and evaluation
Model interpretability and explainability (e.g., SHAP, feature attribution)
4+ years of hands-on experience with deep learning architectures where appropriate
4+ years of experience with time-series analysis and survival analysis
4+ years of experience with vibe coding tools, such as Cursor, Claude Code, and Windsurf
4+ years of experience in healthcare data literacy, including experience with:
Claims, EHR, lab, and pharmacy data
Coding systems such as ICD, CPT, NDC, SNOMED, and LOINC
Interoperability standards such as FHIR and HL7
Reasoning about data quality, missingness, bias, and confounding in healthcare datasets
4+ years of experience as a contributor in complex applied data science initiatives from concept to production
4+ years of experience working in cross-functional environments with engineering, product, and business teams
4+ years of experience balancing model sophistication, interpretability, scalability, and business impact
Advanced level of proficiency in Python for data science and ML (Pandas, NumPy, scikit-learn, PyTorch or equivalent)
Advanced level of SQL skills for complex data transformations and analytical workflows

Nice To Haves

Experience with MLOps practices (deployment, monitoring, retraining, drift detection)
Prior experience in regulated or highly governed environments
Familiarity with cloud platforms and distributed computing (e.g., Spark, Databricks, AWS, GCP, Azure)
Excellent written and verbal communication skills, with the ability to convey technical concepts to non-technical stakeholders

Responsibilities

Participate in the design, development, and deployment of applied machine learning solutions addressing complex business and clinical problems using large-scale healthcare data
Drive the end-to-end model lifecycle: problem framing, feature engineering, model development, evaluation, validation, explainability, deployment, and post-production monitoring
Develop and review production-grade Python code following software engineering best practices (testing, modularization, version control, CI/CD)
Support architecting scalable data science workflows using Python, SQL, and distributed data processing frameworks in cloud or enterprise environments
Apply and advance classical ML, deep learning, time-series modeling, and survival analysis techniques based on business needs
Ensure models are interpretable, explainable, and compliant with enterprise governance, regulatory, and ethical standards (e.g., bias, fairness, auditability)
Partner with engineering, product, clinical, and business stakeholders to translate ambiguous problems into actionable analytical solutions
Review and approve modeling approaches, assumptions, and results; influence architectural and methodological decisions across teams
Communicate insights, risks, and tradeoffs clearly to technical and executive audiences
Stay current with emerging methods in applied ML, healthcare analytics, and MLOps, and drive adoption of best practices