Note: The job is a remote job and is open to candidates in USA. Optum is a global organization focused on improving health outcomes through technology. The Data Scientist role involves designing and deploying machine learning and generative AI solutions to prevent fraud in healthcare claims, collaborating with various teams to translate business challenges into scalable solutions.
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
Skills
- 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
- 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
- A comprehensive benefits package
- Incentive and recognition programs
- Equity stock purchase
- 401k contribution (all benefits are subject to eligibility requirements)
Company Overview