Principal Applied Scientist

Oracle

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Work type
On-site
Location
—
Salary
$126,200–$264,100 / yr
Posted
12 days ago
Freshness
Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $225k
This role $195k
$108k $293k
below market most similar roles pay here above market

This role pays less than 70% of similar roles. Most pay $193,220–$256,850 — the blue band above. At the midpoint, this role pays about $195k versus about $225k for comparable roles.

Based on 240 similar postings.

Employer

About Oracle

Oracle Corporation is a leading multinational technology company specializing in database software, cloud computing, and enterprise software.

Oracle currently has 955 open roles on FindRole.

Listed pay typically runs $102,300–$234,600 across 790 roles with salary data.

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At a glance

TL;DR · Principal Applied Scientist

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JOB TITLE: Principal Applied Scientist The Principal Applied Scientist joins the Oracle Health Data Intelligence team to design and build production AI systems, specifically focusing on agents powered by large language models. You will lead the development of agent workflows that integrate reasoning, tool use, and information retrieval to solve complex healthcare and life sciences problems. Daily responsibilities include writing production-quality Python code, establishing rigorous evaluation methods for model accuracy and safety, and performing statistical analysis to improve system performance. You will collaborate with engineering and product teams to translate user needs into scalable solutions. Key technical requirements include experience with generative AI, LLM-based systems, retrieval-augmented generation, and frameworks like LangGraph. This role addresses the need for reliable, privacy-sensitive AI in clinical and operational workflows where accuracy and trust are paramount.

What does a Applied Scientist earn?

Median $208800 from 61 postings across 10 companies.

See salary data

Skills

Generative AI LLMs Python LangGraph Retrieval-Augmented Generation Natural Language Processing Machine Learning Statistical Modeling AI Agents Information Retrieval Model Adaptation LLM Evaluation System Design Data Science

What you'll do

  • Lead the design, development, evaluation, and deployment of LLM-based agents for HDI products.
  • Build agent workflows that combine reasoning, tool use, and information retrieval for healthcare needs.
  • Develop production-quality code and collaborate on system design, integration, performance, and monitoring.
  • Establish practical evaluation methods for agent behavior, including accuracy, reliability, safety, and latency.
  • Apply statistical analysis and traditional machine learning methods to improve system performance and explain results.
  • Investigate errors and unexpected behavior to identify root causes and implement measurable improvements.
  • Translate user needs into scalable, responsible AI solutions by partnering with product and security teams.
  • Set technical direction for complex projects and mentor scientists and engineers through design and code reviews.

What we're looking for

  • Applicants must have 10 years of relevant experience in data science, machine learning, AI, NLP, or statistical modeling.
  • Applicants must have a bachelor’s degree and 7 years of relevant experience, a master’s degree and 5 years, or a doctorate and 3 years.
  • Applicants must have strong programming skills, including at least 3 years of experience in an applicable language like Python.
  • Applicants must have hands-on experience developing LLM or generative AI applications and assessing output quality.
  • Applicants must have experience designing experiments, performing statistical analysis, and evaluating AI or machine learning systems.
  • Applicants must have strong written and verbal communication skills and the ability to lead ambiguous technical work.
  • Applicants must be able to read, write, and speak English.
  • Healthcare experience, experience building AI agents, and experience with retrieval-augmented generation are preferred.

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