Applied AI Researcher

Role overview Develop applied AI systems that uncover and validate financial opportunities in complex healthcare contracts, spending, and operational data. You’ll work with engineering, product, and domain specialists to turn hands-on analysis into reliable workflows, with success measured by both technical performance and the value identified.

Responsibilities - Build and improve AI agents that find opportunities across healthcare supply chain, pharmacy, and managed care. - Analyze large, messy datasets to identify patterns, anomalies, and relationships relevant to pricing, contracts, underpayments, and negotiations. - Translate expert-led analysis into automated workflows that improve the speed, accuracy, and scale of opportunity discovery. - Develop information extraction and agent workflows for complex healthcare documents, then work with engineers to make successful approaches usable in a product. - Set evaluation methods and performance measures, investigate failures, and improve models based on results. - Collaborate with technical and domain teams to identify further automation opportunities and improve data models.

Requirements - Roughly 2–8 years of experience in applied AI, data science, machine learning engineering, or a related field; demonstrated ability matters alongside tenure. - Proficiency in Python and a solid grounding in statistics, experimental design, and data modeling. - Experience building, debugging, and improving LLM-powered agents or workflows. - Ability to investigate unfamiliar data, form evidence-based hypotheses, and evaluate model performance systematically. - Sound technical judgment and a willingness to understand underlying data and failure modes, including when using AI tools. - Strong ownership, clear communication, and the ability to work across technical and nontechnical teams in a fast-moving environment.

Nice to have Interest in healthcare operations and applying emerging AI methods to complex, practical problems.