Principal Applied Scientist

Oracle Corporation; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Oracle Health Data Intelligence is seeking a Principal Applied Scientist to lead the development of scalable ML and AI solutions across healthcare product and platform challenges.

This hands-on technical leadership role turns ambiguous, high-impact problems into production-ready models and robust systems, with a focus on applied AI/ML beyond a single technique, and a strong engineering bar.

Responsibilities

• Lead end-to-end development of ML/AI solutions: problem formulation, data analysis, feature development, modeling, offline evaluation, experimentation, deployment, and monitoring.
• Design and build production-grade Python code; write scalable software, not just prototypes.
• Apply techniques across ML, DL, NLP, information retrieval, ranking, forecasting, anomaly detection, optimization, generative AI, and LLM-based systems.
• Develop evaluation frameworks for 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.

Skills

Machine Learning

Python

Applied AI

MLOps

Leadership

Tools

Git

Docker

ML libraries (TensorFlow/PyTorch)

Job description

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.

What You’ll Do

• 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.