Join Optum, a global leader in health optimization, and contribute to impactful AI-driven initiatives. This role is crucial for advancing healthcare technologies, focusing on AI-powered prediction, understanding patient intent, and enhancing patient navigation through our Digital Front Door.

Your work will directly influence business success by improving the accuracy of patient estimates, optimizing prior authorization workflows, and minimizing claim denials. This is an opportunity to be part of a culture that values inclusion, collaboration, and continuous growth.

Take full ownership of the machine learning model lifecycle, from problem definition and stakeholder collaboration to feature engineering from diverse healthcare data.

Train, evaluate, and deploy models into production, ensuring they are monitored and maintained.

Develop production-quality Python code, adhering to best software engineering practices like version control and code review.

Implement deep learning and transformer-based techniques, alongside generative AI and LLM components, utilizing prompting, fine-tuning, and retrieval-augmented methods.

Collaborate with MLOps and engineering teams to deploy models as robust services or APIs, providing post-release support.

Design and execute rigorous experiments, clearly communicating results, trade-offs, and validation strategies to both technical and non-technical stakeholders.

Partner with product managers and data engineers to translate business needs into well-defined modeling challenges.

Contribute to model documentation, champion responsible AI practices, and ensure readiness for regulatory governance.

Possess a Bachelor's, Master's, or PhD in Computer Science, AI, Machine Learning, Data Science, Statistics, or a related quantitative field.

Demonstrate 5+ years of applied experience in building machine learning models that address real-world business challenges.

Showcase hands-on experience with generative AI or LLMs, including techniques such as prompting, fine-tuning, embeddings, and retrieval-based approaches.

Have practical experience with deep learning frameworks like PyTorch or TensorFlow.

Proven success in deploying at least one model or system as a production service or API and providing ongoing support.

Exhibit strong Python programming skills, emphasizing the creation of maintainable, production-grade code.

Maintain a solid foundation in classical machine learning, including tree-based and gradient-boosted methods, with a strong grasp of feature engineering, model evaluation, and validation techniques.

Clearly communicate modeling decisions, assumptions, and outcomes to diverse cross-functional teams.