About the job
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.
Responsibilities
Lead the design, development, and deployment of applied machine learning solutions addressing complex business and clinical problems using large-scale healthcare data
Own 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)
Architect 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
Provide technical leadership and mentorship to senior and mid-level data scientists; set standards for modeling, experimentation, and code quality
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
Qualifications
Minimum
7+ years of experience building production-quality, maintainable, and testable code
7+ years of experience in 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)
Hands-on experience with deep learning architectures where appropriate
Strong applied experience with time-series analysis and survival analysis
Experience with vibe coding tools, such as Cursor, Claude Code, and Windsurf
7+ 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
7+ years of experience leading complex applied data science initiatives from concept to production
7+ years of experience mentoring senior data scientists and elevate technical standards
7+ years of experience working in cross-functional environments with engineering, product, and business teams
7+ years of experience balancing model sophistication, interpretability, scalability, and business impact
Advanced level 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
Preferred
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)