Data Scientist

UnitedHealth Group•Minnetonka, MN

6d•Remote

About The Position

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.

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