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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist 2 - **Company:** Unitedhealth Group Inc - **Location:** Minneapolis, MN, United States - **Salary:** $72,800.0 - $130,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Application Integration Architecture, Automation of Tests, Microsoft Azure, Cloud Computing, Cloud Engineering, Code Review, Continuous Integration, Data Validation, Distributed Systems, Monitoring of Systems, Statistical Hypothesis Testing, Information Retrieval, Python (Programming Language), Machine Learning, Software Architecture, Query Optimization, Tensorflow, Software Safety, Search Technologies, Software Deployment, SQL Databases, Data Logging, Pytorch, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Model Validation, Generative AI, Agentic-AI, Git, Fastapi, Matplotlib, Containerization, Data Lakes, Pyspark, Scikit Learn, Kubernetes, Xgboost, Performance Monitor, Machine Learning Operations, Drift Detection, Restful APIs, Semantic Kernel, Software Version Control, Software Library, Docker, Databricks - **Published:** October 28, 2026 - **Apply:** https://www.minneapoliscareersite.com/job.asp?id=3477628583&tx=FL939FFR&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * 2+ years of professional experience delivering Data Science, Machine Learning, Time Series forecasting, AI Engineering for production grade solutions * 2+ years of hands-on proficiency in Python for data analysis, model development, application integration, and production engineering * 2+ years of experience with SQL, including complex joins, aggregations, window functions, data validation, query optimization, and analytical dataset creation * 2+ years of experience using machine learning libraries such as scikit-learn, XGBoost, PyTorch, or TensorFlow, Seaborn, Scikit-learn, MLflow * 2+ years of experience with distributed computing and cloud platforms (e.g., Apache Spark/PySpark, Azure, Azure Databricks) * 1+ years of experience with APIs, application integration, Git, automated testing, CI/CD pipelines, and production support practices * Ability to leverage statistical and analytical techniques, including probability, hypothesis testing, regression, model evaluation, and experimental methods, to develop and evaluate robust solutions * Experience with model-performance monitoring, data-quality checks, data drift, model drift, logging, and troubleshooting * Ability to explain individual technical contributions, architectural decisions, implementation challenges, and measurable outcomes * Demonstrated experience operationalizing and deploying models to production beyond proof-of-concept stages, * Experience delivering healthcare analytics or AI solutions in clinical operations, population health, patient flow, utilization, or financial performance domains * Deep experience with Azure Databricks capabilities (Unity Catalog, Delta Lake, Workflows, Model Serving, MLflow, Databricks Apps, or Asset Bundles) * Hands-on experience developing LLM, Retrieval-Augmented Generation (RAG), semantic search, vector retrieval, or Agentic AI applications using tools such as LangChain, LangGraph, or Semantic Kernel * Experience building REST APIs (FastAPI or Flask) and containerized deployments using Docker or Kubernetes * Familiarity with Responsible AI practices, model interpretability, bias assessment, AI safety, and privacy-preserving analytics in regulated healthcare environments ## Description Optum Insight COE Advanced Analytics team is seeking an experienced, hands-on Data Scientist 2 to design, develop, deploy, and support enterprise-grade analytics, ML, Gen AI, and Agentic AI solutions for complex healthcare and operational use cases. The successful candidate will combine solid foundations in data science, AI engineering, statistics, Python, SQL, machine learning, and cloud engineering with practical experience taking solutions from business problem definition and experimentation through production deployment, monitoring, and continuous improvement. This role works closely with healthcare domain experts, product owners, data engineers, application engineers, platform teams, architects, and business stakeholders. The portfolio includes predictive and prescriptive analytics, patient-flow and operational forecasting, healthcare performance improvement, MLOps, model monitoring, natural-language analytics, Retrieval-Augmented Generation, multi-agent applications, and Responsible AI. Primary Responsibilities: * Translate complex healthcare, clinical, operational, and financial business problems into well-defined analytical questions, hypotheses, metrics, and solution designs * Design, develop, evaluate, and optimize machine learning models for classification, regression, time-series forecasting, recommendation, and predictive/prescriptive use cases * Design and implement enterprise-grade Generative AI applications using large language models, embeddings, Retrieval-Augmented Generation (RAG), vector retrieval, prompt engineering, and multi-agent workflows * Develop Agentic AI and multi-agent workflows involving routing, planning, tool invocation, memory, reflection, and supervisor-worker orchestration * Implement guardrails, validation, traceability, access controls, and human-review mechanisms for enterprise AI solutions * Own the end-to-end lifecycle of machine learning and AI solutions, moving models from experimentation through testing, deployment, MLOps, monitoring, drift detection, and continuous improvement * Build and maintain scalable data-processing workflows, feature tables, Delta tables, and model pipelines using Python, SQL, Spark, and Databricks * Apply software-engineering practices including modular code design, Git-based version control, automated testing, code reviews, logging, and technical documentation * Apply statistical methods, hypothesis testing, and experimental design to structured and unstructured healthcare data, ensuring clear differentiation between correlation and causation * Ensure compliance with Responsible AI practices, including fairness evaluation, explainability, hallucination assessment, output validation, and secure handling of sensitive healthcare data * Partner with cross-functional technical and business stakeholders to break complex work into user stories, technical milestones, architectural designs, and actionable analytical findings 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.