Principal Applied Scientist

Oracle Health Data Intelligence is building AI-powered products that help healthcare organizations make better, faster, and more informed decisions.

We are seeking a Principal Applied Scientist to lead the development of scalable machine learning and AI solutions across a broad range of product and platform challenges.

This is a hands-on technical leadership role for an experienced scientist who can turn ambiguous, high-impact business and customer problems into robust, production-ready models and systems.

The role is broadly focused on applied AI/ML—not limited to a single technique such as LLMs—and has a strong engineering and coding bar comparable to a senior/principal Applied Scientist role.

• Lead the end-to-end development of machine learning and AI solutions: problem formulation, data analysis, feature development, modeling, offline evaluation, experimentation, deployment, and monitoring.
• Design and build high-quality, maintainable production code in Python and related technologies. Write scalable, testable software rather than research-only prototypes.
• Apply appropriate techniques across machine learning, deep learning, NLP, information retrieval, ranking, forecasting, anomaly detection, optimization, generative AI, and LLM-based systems.
• Develop evaluation frameworks that measure model quality, reliability, safety, fairness, latency, and business or clinical impact.
• Drive rigorous experimentation, including experiment design, statistical analysis, error analysis, ablation studies, and root-cause investigation.
• Partner with software engineers to productionize models, define service interfaces, improve inference performance, and establish monitoring and retraining workflows.
• Work closely with product, data engineering, clinical domain, security, and compliance partners to translate real-world needs into durable AI capabilities.
• Set technical direction for complex initiatives, make sound tradeoffs under ambiguity, and influence roadmap decisions through data and scientific judgment.
• Mentor applied scientists and engineers through design reviews, code reviews, technical guidance, and modeling best practices.
• Stay current with relevant research and evaluate emerging methods pragmatically, adopting innovations when they create measurable product value.