Principal Applied Scientist

Oracle Health Data Intelligence (HDI) is building AI-powered solutions that help healthcare organizations improve population health. We are seeking a Principal Applied Scientist to develop advanced AI capabilities, with an emphasis on large language models, agentic systems, model post-training, and evaluation.
This is a hands-on individual contributor role for an experienced scientist who can translate complex problems into reliable, production-ready systems. You will combine scientific rigor with strong software engineering to develop models, build agents, and implement training and evaluation infrastructure.
Minimum Job Qualifications
Education and/or Experience:
11 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field
OR
Bachelor's Degree in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 7 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field
OR
Master's Degree in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 5 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field
OR
Doctorate in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 3 year of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field.
Job Skills:
Same skills as prior level plus;
Predictive Analytics Demonstrated expertise in building and applying predictive analytics to identify trends and inform strategic decisions.
Natural Language Processing Demonstrated ability to utilize NLP techniques and frameworks for advanced language data processing.
Statistical Analysis Demonstrated ability to conduct statistical analyses and interpret business or research data.
Machine Learning Frameworks Demonstrated ability to use machine learning frameworks to develop and optimize models for real-world business scenarios.
Generative Artificial Intelligence (GenAI) Model Building Demonstrated ability in or knowledge of GenAI model building, including training, fine-tuning, and monitoring generative AI solutions.
Data Analysis Demonstrated ability to analyze and interpret data to produce actionable business insights.
Research Demonstrated ability in or knowledge of research, including gathering information, formulating questions, and interpreting results to address objectives.
Research Planning and Literature Review Demonstrated ability in or knowledge of research planning and literature review, including comprehending and critiquing previous research to inform future work.
Programming Language:
3 years of experience in applicable programming language.
Preferred Job Qualifications
Education and/or Experience:
12 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field
OR
Bachelor's Degree in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 8 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field
OR
Master's Degree in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 6 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field
OR
Doctorate in Mathematics or Statistics, Computer Science, Data Science, Physics, or related field AND 4 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or related field.
Academic Writing and Publication:
3 years of experience creating publications (e.g., white papers, peer-reviewed conference or journal articles).
What You’ll Do
Develop AI/ML solutions across the full lifecycle, from problem formulation and experimentation through deployment and monitoring.
Design and build LLM-based agents, including reasoning, planning, retrieval, tool use, and orchestration.
Apply model post-training techniques, including supervised fine-tuning, preference optimization, and reinforcement learning where appropriate.
Build high-quality datasets and feedback pipelines for training, evaluation, and continuous improvement.
Implement agent and evaluation harnesses that support reproducible experimentation, trace analysis, benchmarking, and regression testing.
Evaluate model and agent quality, reliability, safety, latency, and cost using automated methods and expert feedback.
Conduct rigorous error analysis and controlled experiments to identify failure modes and improve performance.
Write scalable, maintainable production code in Python and work with engineers to operationalize AI systems.
Collaborate with product, clinical, data engineering, security, and compliance partners to understand requirements and assess results.
Contribute to technical discussions, design reviews, and code reviews.
Evaluate emerging research and apply methods that deliver measurable improvements.
Basic Qualifications
PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience; or a Master’s degree with 6+ years of relevant industry experience.
10+ years of experience applying machine learning, data science, or AI to real-world problems.
Strong Python programming skills, including experience writing efficient, production-quality code with appropriate testing and documentation.
Experience building and deploying LLM-based systems, including agents, retrieval, and tool use.
Hands-on experience with model post-training, such as supervised fine-tuning, preference optimization, or reinforcement learning.
Experience developing evaluation frameworks, benchmarks, and experimental infrastructure for models and agents.
Deep understanding of machine learning, optimization, statistical inference, and experimental design.
Experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent tools.