• Own the supply side of Protege's data platform • Define the stages, validation gates, and quality checks that data passes through • Own the product decisions around metadata generation • Define what 'catalog-ready' means and build the tooling that enforces it • Work with vertical stakeholders to translate their requirements into platform-level standards

• Design, build, and deploy foundational GTM models, including Customer Lifetime Value (LTV) forecasting, Marketing and Sales Attribution, and Propensity models. • Partner with GTM teams to design and analyze controlled experiments across various channels, including website A/B testing, pricing experiments, and marketing campaign effectiveness. • Execute proactive, complex analytical deep dives to discover latent user behavior and root causes of changes in GTM metrics, translating findings into actionable recommendations. • Support the interpretation of Marketing Mix Modeling (MMM) results to help maximize marketing ROI and assess the feasibility of future in-house modeling. • Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps GTM activities to high-level business outcomes. • Translate complex statistical findings and model outputs into compelling business narratives for cross-functional partners. • Work closely with Data Engineering to ensure data quality, reliable instrumentation, and the development of reusable predictive assets like model feature stores. • Provide technical guidance to peers and stakeholders on best practices for data exploration, ML modeling, and causal methodologies.

• Oversee the design, development, and implementation of scalable scoring models • Collaborate with the Credit team to define data science strategies • Guide and mentor junior data scientists • Communicate complex technical concepts to non-technical stakeholders • Establish rigorous testing and validation protocols • Stay on top of industry trends and emerging technologies • Work closely with product, engineering, and business teams to integrate data science outputs

Role Description 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. Primary 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. Qualifications - 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. Preferred Qualifications - 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). Benefits - Comprehensive benefits package. - Incentive and recognition programs. - Equity stock purchase. - 401k contribution (all benefits are subject to eligibility requirements). Application Deadline This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.