Data Scientist

UnitedHealth Group•Minnetonka, MN

11h•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. We are the UHC Payment Integrity AI/ML Engineering Team, responsible for designing and deploying advanced Machine Learning and Generative AI solutions that prevent Fraud, Waste, and Abuse (FWA) across the healthcare claims ecosystem. Our work spans pre‑payment prediction, post‑payment anomaly detection, intelligent automation, and next‑generation LLM‑based decisioning. The team is a blend of AI/ML engineers, data scientists, data engineers, and GenAI specialists who collaborate to build and scale end‑to‑end ML systems. We align closely with the organization’s mission of improving affordability, operational efficiency, and trust in healthcare data You will enjoy the flexibility to telecommute from anywhere within the U.S. as you take on some tough challenges. 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

Bachelor's degree in CS or IT related field
5+ years of hands‑on experience in AI/ML engineering, deep learning, or applied machine learning
3+ years of experience in Python, PySpark, ML frameworks (TensorFlow, PyTorch), and distributed training
3+ years of experience with big‑data systems (like Hadoop, Spark, Hive) and cloud platforms (like Azure, AWS, GCP)
2+ years of experience with LLMs, including: Finetuning (LoRA, QLoRA, PEFT, SFT, or RLHF) Prompt engineering & system design RAG pipelines & vector search

Nice To Haves

Prior experience with US healthcare datasets (claims, clinical, EMR/EHR, provider networks, payer ops)
Experience deploying ML/LLM workloads using Databricks, MLflow, Kubernetes, or serverless inference
Familiarity with modern GenAI tooling (LangChain, LlamaIndex, HuggingFace, OpenAI/Anthropic/Azure‑OpenAI APIs)
Knowledge of deep learning architectures (Transformers, sequence models, contrastive learning)
Experience optimizing model inference using quantization, distillation, or distributed GPU compute
Demonstrated success in AI product delivery, cross‑functional collaboration, and influencing technical strategy
Strong grounding in ML fundamentals (feature engineering, model evaluation, A/B testing, MLOps best practices)

Responsibilities

Design, train, finetune, and deploy Large Language Models (LLMs) and Generative AI components for claims automation, anomaly detection, and investigative workflows
Build and operationalize ML pipelines using Python, PySpark, and cloud-native architectures (Azure/AWS/GCP)
Develop traditional machine learning models (classification, anomaly detection, NLP pipelines) for high‑volume healthcare datasets
Implement RAG (Retrieval‑Augmented Generation) systems, embedding models, and vector database integrations
Develop automated data processing, feature engineering, and model training pipelines using Spark, MLflow, Databricks, and big‑data ecosystems
Partner with product, engineering, and clinical domain teams to translate complex business challenges into scalable ML and GenAI solutions
Optimize and monitor ML models in production, ensuring accuracy, latency, compliance, and responsible‑AI best practices
Present AI/ML solution designs, model insights, and GenAI architecture recommendations to technical and non‑technical stakeholders
Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI