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.
Internal Responsibilities
Basic Qualifications
- PhD in Computer Science, Machine Learning, Statistics, Operations Research, a related quantitative field, or equivalent practical experience; OR a Master’s degree with 6+ years of relevant industry experience.
- 8+ years of experience applying machine learning, data science, or AI techniques to real-world product or business problems.
- Strong programming ability in Python, including writing clean, efficient, production-quality code with appropriate testing and documentation.
- Experience building and shipping machine learning systems, including data pipelines, training workflows, inference services, evaluation, and model monitoring.
- Deep understanding of core machine learning concepts, such as supervised and unsupervised learning, optimization, representation learning, model selection, experimentation, and statistical inference.
- Experience with one or more ML frameworks such as PyTorch, TensorFlow, JAX, scikit-learn, Spark, or equivalent tools.
- Demonstrated ability to independently lead ambiguous technical projects and influence cross-functional stakeholders.
- Strong written and verbal communication skills, including the ability to explain technical decisions to technical and non-technical audiences.
Preferred Qualifications
- Experience with generative AI, large language models, retrieval systems, RAG, agentic workflows, or model fine-tuning and evaluation.
- Experience with NLP, search, recommendation, ranking, time-series modeling, causal inference, or optimization.
- Experience designing AI systems for high-reliability, privacy-sensitive, regulated, or customer-facing environments.
- Experience with cloud-scale distributed systems and ML platforms.
- Experience mentoring senior technical contributors and raising engineering and scientific standards across a team.
- Healthcare, life sciences, enterprise SaaS, or similarly complex-domain experience.
Level: IC4
External Responsibilities
Basic Qualifications
- PhD in Computer Science, Machine Learning, Statistics, Operations Research, a related quantitative field, or equivalent practical experience; OR a Master’s degree with 6+ years of relevant industry experience.
- 8+ years of experience applying machine learning, data science, or AI techniques to real-world product or business problems.
- Strong programming ability in Python, including writing clean, efficient, production-quality code with appropriate testing and documentation.
- Experience building and shipping machine learning systems, including data pipelines, training workflows, inference services, evaluation, and model monitoring.
- Deep understanding of core machine learning concepts, such as supervised and unsupervised learning, optimization, representation learning, model selection, experimentation, and statistical inference.
- Experience with one or more ML frameworks such as PyTorch, TensorFlow, JAX, scikit-learn, Spark, or equivalent tools.
- Demonstrated ability to independently lead ambiguous technical projects and influence cross-functional stakeholders.
- Strong written and verbal communication skills, including the ability to explain technical decisions to technical and non-technical audiences.
Preferred Qualifications
- Experience with generative AI, large language models, retrieval systems, RAG, agentic workflows, or model fine-tuning and evaluation.
- Experience with NLP, search, recommendation, ranking, time-series modeling, causal inference, or optimization.
- Experience designing AI systems for high-reliability, privacy-sensitive, regulated, or customer-facing environments.
- Experience with cloud-scale distributed systems and ML platforms.
- Experience mentoring senior technical contributors and raising engineering and scientific standards across a team.
- Healthcare, life sciences, enterprise SaaS, or similarly complex-domain experience.
Level: IC4
