Principal Applied Scientist

Oracle Health Data Intelligence (HDI) uses data and AI to help healthcare and life sciences organizations turn complex information into useful insights and better decisions. Our teams build products for challenges across clinical, operational, and life sciences workflows.

We are seeking a Principal Applied Scientist to design and build production AI systems, with a particular focus on agents powered by large language models (LLMs). You will help determine how agents reason, use tools, retrieve information, and complete complex tasks reliably. This is a hands-on role for someone who can move from an ambiguous customer problem to a working product capability.

The ideal candidate brings strong experience with generative AI and LLM-based systems, along with the machine learning and statistical skills needed to evaluate them rigorously. You will work closely with engineering, product, and domain experts to make sound technical choices in an environment where accuracy, privacy, and trust matter.

• Lead the design, development, evaluation, and deployment of LLM-based agents and other AI capabilities for HDI products.
• Build agent workflows that combine reasoning, tool use, information retrieval, and appropriate human review to address real-world healthcare and life sciences needs.
• Develop production-quality code and collaborate with engineers on system design, integration, performance, monitoring, and ongoing improvement.
• Establish practical evaluation methods for agent behavior and model outputs, including task success, accuracy, reliability, safety, latency, and failure modes.
• Apply statistical analysis, experimentation, and traditional machine learning methods where they improve system performance or help explain results.
• Investigate errors and unexpected behavior, identify root causes, and turn findings into measurable improvements.
• Partner with product, engineering, clinical, data, security, and compliance colleagues to translate user needs into scalable, responsible AI solutions.
• Set technical direction for complex projects, communicate tradeoffs clearly, and mentor scientists and engineers through design and code reviews.

Assess emerging approaches and frameworks, adopting them when they provide clear value for customers.

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Education and experience — one of the following:

• 10 years of relevant experience in data science, machine learning, artificial intelligence, natural language processing, statistical modeling, or a related field; or
• A bachelor’s degree in a related quantitative or technical field and 7 years of relevant experience; or
• A master’s degree in a related quantitative or technical field and 5 years of relevant experience; or

A doctorate in a related quantitative or technical field and 3 years of relevant experience.

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Additional qualifications:

• Strong programming skills, including at least 3 years of experience in an applicable programming language. Experience writing maintainable, production-quality Python code is especially relevant.
• Hands-on experience developing LLM or generative AI applications, including the ability to assess output quality and system behavior.
• Experience designing experiments, performing statistical analysis, and evaluating AI or machine learning systems.
• Ability to lead ambiguous technical work and collaborate effectively across science, engineering, and product teams.

Strong written and verbal communication skills.

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Preferred Qualifications

• Healthcare experience is desired , including work with healthcare data, clinical or operational workflows, or AI products used by healthcare organizations. Relevant life sciences, pharmaceutical, or clinical trials experience is also valuable.
• Experience building AI agents, including tool use, orchestration, multi-step reasoning, and evaluation. Familiarity with frameworks such as LangGraph or comparable agent development tools is a plus.
• Experience with retrieval-augmented generation, information retrieval, model adaptation, or LLM evaluation.
• Experience developing AI systems for privacy-sensitive, regulated, or high-reliability environments.
• Experience in healthcare, life sciences, pharmaceuticals, or clinical trials. Domain experience is welcome but not required.
• Experience mentoring other technical contributors and influencing product or technical direction.

At least 3 years of experience creating technical or academic publications, such as white papers or peer-reviewed conference or journal articles.

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Career Level - IC4