Data Scientist 2

UnitedHealth Group; Plymouth (MN); On-site; USD 73,000 - 130,000; Full time

Benefits offered by this job

Comprehensive benefits package

Incentive and recognition programs

Equity stock purchase

401k contribution

Job summary

UnitedHealth Group's Optum Insight COE Advanced Analytics seeks a hands-on Data Scientist 2 to design, build, deploy, and support enterprise analytics and AI solutions for healthcare and operational use cases. You will own end-to-end model lifecycles, build robust pipelines, and apply responsible AI practices across projects.

You will work with Python, SQL, Spark, and Databricks to develop ML solutions, MLOps, and Generative AI applications, collaborating with cross-functional teams to translate

Qualifications

• 2+ years of professional experience delivering Data Science, Machine Learning, Time Series forecasting, or 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 SQL experience, including complex joins, aggregations, window functions, data validation, query optimization, and analytical dataset creation
• 2+ years using machine learning libraries including scikit-learn, XGBoost, PyTorch, or TensorFlow, plus Seaborn and scikit-learn, and experience with MLflow
• 2+ years experience with distributed computing and cloud platforms such as Apache Spark/PySpark and Azure (including Azure Databricks)
• 1+ years of experience with APIs and application integration, Git, automated testing, CI/CD pipelines, and production support practices
• Ability to apply statistical and analytical techniques including probability, hypothesis testing, regression, model evaluation, and experimental methods
• Experience with model-performance monitoring, data-quality checks, data drift, model drift, logging, and troubleshooting
• Ability to explain technical contributions, architectural decisions, implementation challenges, and measurable outcomes
• Demonstrated ability to operationalize and deploy models to production beyond proof-of-concept

Responsibilities

• Convert complex healthcare, clinical, operational, and financial problems into analytical questions, hypotheses, metrics, and solution designs
• Design, develop, evaluate, and optimize machine learning models for classification, regression, time-series forecasting, recommendation, and predictive and prescriptive use cases
• Build enterprise 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 including routing, planning, tool invocation, memory, reflection, and supervisor-worker orchestration
• Implement enterprise AI guardrails, validation, traceability, access controls, and human-review mechanisms
• Own end-to-end model lifecycle activities, including transition from experimentation through testing, deployment, MLOps, monitoring, drift detection, and continuous improvement
• Create and maintain scalable data-processing workflows, feature tables, Delta tables, and model pipelines using Python, SQL, Spark, and Databricks
• Apply software engineering practices such as modular code design, Git-based version control, automated testing, code reviews, logging, and technical documentation
• Use statistical methods, hypothesis testing, and experimental design for structured and unstructured healthcare data, with clear differentiation between correlation and causation
• Support Responsible AI practices including fairness evaluation, explainability, hallucination assessment, output validation, and secure handling of sensitive healthcare data
• Collaborate with cross-functional technical and business stakeholders to translate work into user stories, technical milestones, architectural designs, and actionable analytical findings

Skills

Python

SQL

ML libraries

Spark/PySpark

Azure Databricks

Git & CI/CD

API development

Docker/Kubernetes

MLOps

LangChain/RAG

Tools

Azure Databricks

Spark

MLflow

Docker

Kubernetes

FastAPI/Flask

Job description

Optum Insight COE Advanced Analytics seeks a hands‑on Data Scientist 2 to design, build, deploy, and support enterprise analytics and AI solutions for healthcare and operational use cases. The role covers the full ML and AI lifecycle, including MLOps, model monitoring, continuous improvement, and Generative AI and Agentic AI capabilities.

Responsibilities

• Convert complex healthcare, clinical, operational, and financial problems into analytical questions, hypotheses, metrics, and solution designs
• Design, develop, evaluate, and optimize machine learning models for classification, regression, time-series forecasting, recommendation, and predictive and prescriptive use cases
• Build enterprise 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 including routing, planning, tool invocation, memory, reflection, and supervisor-worker orchestration
• Implement enterprise AI guardrails, validation, traceability, access controls, and human-review mechanisms
• Own end-to-end model lifecycle activities, including transition from experimentation through testing, deployment, MLOps, monitoring, drift detection, and continuous improvement
• Create and maintain scalable data-processing workflows, feature tables, Delta tables, and model pipelines using Python, SQL, Spark, and Databricks
• Apply software engineering practices such as modular code design, Git-based version control, automated testing, code reviews, logging, and technical documentation
• Use statistical methods, hypothesis testing, and experimental design for structured and unstructured healthcare data, with clear differentiation between correlation and causation
• Support Responsible AI practices including fairness evaluation, explainability, hallucination assessment, output validation, and secure handling of sensitive healthcare data
• Collaborate with cross-functional technical and business stakeholders to translate work into user stories, technical milestones, architectural designs, and actionable analytical findings

Requirements

• 2+ years of professional experience delivering Data Science, Machine Learning, Time Series forecasting, or 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 SQL experience, including complex joins, aggregations, window functions, data validation, query optimization, and analytical dataset creation
• 2+ years using machine learning libraries including scikit-learn, XGBoost, PyTorch, or TensorFlow, plus Seaborn and scikit-learn, and experience with MLflow
• 2+ years experience with distributed computing and cloud platforms such as Apache Spark/PySpark and Azure (including Azure Databricks)
• 1+ years of experience with APIs and application integration, Git, automated testing, CI/CD pipelines, and production support practices
• Ability to apply statistical and analytical techniques including probability, hypothesis testing, regression, model evaluation, and experimental methods
• Experience with model-performance monitoring, data-quality checks, data drift, model drift, logging, and troubleshooting
• Ability to explain technical contributions, architectural decisions, implementation challenges, and measurable outcomes
• Demonstrated ability to operationalize and deploy models to production beyond proof-of-concept

Technologies

Python, SQL, scikit-learn, XGBoost, PyTorch, TensorFlow, Seaborn, MLflow, Apache Spark, PySpark, Azure, Azure Databricks, Git, CI/CD, API, Delta tables, Retrieval-Augmented Generation (RAG), vector retrieval, large language models, LangChain, LangGraph, Semantic Kernel, FastAPI, Flask, Docker, Kubernetes, Unity Catalog, Delta Lake, Workflows, Model Serving, Databricks Apps, Asset Bundles.

Preferred Qualifications

• 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 including 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 such as model interpretability, bias assessment, AI safety, and privacy-preserving analytics in regulated healthcare environments

Benefits

• Comprehensive benefits package
• Incentive and recognition programs
• Equity stock purchase
• 401k contribution (all benefits are subject to eligibility requirements)

Salary for this role ranges from $72,800 to $130,000 annually.

Additional Information

Pay is based on several factors including local labor markets, education, work experience, and certifications (as applicable).

UnitedHealth Group and its affiliated brands are an Equal Employment Opportunity employer under applicable law. Candidates are considered for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, protected veteran status, or other characteristics protected by local, state, or federal laws, rules, and regulations.

UnitedHealth Group and its affiliated brands are a drug-free workplace. Candidates are required to pass a drug test before beginning employment.