Principal Data Scientist

Do you want to be a part of changing healthcare?
Oracle is excited to be using our resources, knowledge, and expertise—as well as our successes in other industries—and applying them to healthcare to make a meaningful impact. As people, we all participate in healthcare, it’s deeply personal, and we put the human at the center of each of our decisions. Improving healthcare for all requires bringing unique perspectives and expertise together to holistically tackle the biggest problems in global health including physician burnout, patient access to data, and barriers to quality care.

Oracle Health Applications & Infrastructure (OHAI) is developing patient- and provider-centric solutions rapidly and securely. We leverage the power of Oracle Cloud Infrastructure (OCI) to deliver robust, scalable solutions across patient, provider, payer, public health, and life sciences sectors. At OHAI, you’ll work with experts across industries and have access to cutting-edge technologies. We apply artificial intelligence, machine learning, large language models, learning networks, and data intelligence in an applied, scalable, and embedded way. Join us in creating people-centric healthcare experiences.

About the Team

As part of the Oracle Health Foundations Organization, you’ll join a high-impact Operational Intelligence team focused on transforming operational data into proactive, AI-driven decision systems. We work across incidents, change records, support tickets, telemetry, and performance signals to improve the reliability, resilience, and stability of Oracle Health’s products.

Our team is advancing anomaly detection, statistical signal modeling, and GenAI-powered intelligence to move beyond reactive alerting toward contextual insight generation and semi-autonomous operational workflows. We are building scalable capabilities on the Oracle AI Data Platform that detect performance abnormalities early, surface root-cause insights, reduce operational toil, and enable proactive intervention at scale.

As a Senior Data Scientist, you will play a leading role in shaping the evolution of our operational AI systems. You will design and productionize advanced anomaly detection models, develop intelligent data products, and collaborate closely with engineering, platform, and operations teams. This is a high-ownership, high-visibility role where applied statistical rigor meets real-world production impact — helping define the future of agentic operational intelligence within Oracle Health.

Qualifications

• Master's or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative field (Bachelor’s with equivalent experience considered).

• 8+ years of experience developing and deploying machine learning models in production environments.

• Strong expertise in statistical modeling, anomaly detection, and time-series analysis, with experience applying these techniques to large-scale operational data.

• Proficiency in Python and modern ML frameworks, with experience building reproducible modeling pipelines and implementing sound evaluation and experimentation practices.

• Working knowledge of large language models (LLMs), retrieval-augmented generation (RAG) pipelines, and modern agent frameworks, including their evaluation and integration into production systems.

• Experience contributing to intelligent automation or agent-driven systems where ML or LLM components inform or trigger workflow decisions under defined guardrails.

• Strong communication skills and demonstrated ability to partner with engineering teams to productionize, monitor, and continuously improve models.

Responsibilities

• Design, develop, and lead advanced anomaly detection and statistical modeling initiatives across operational data sources (incidents, change records, support tickets, telemetry, and performance signals).
• Build and productionize machine learning models that proactively identify performance abnormalities, detect emerging risk patterns, and improve operational resilience.
• Develop GenAI-powered intelligence capabilities, including contextual insight generation, automated summarization, signal correlation, and decision-support workflows.
• Drive the evolution from deterministic alerting to statistically robust, scalable detection and semi-autonomous operational workflows under defined guardrails.
• Own the end-to-end data science lifecycle, including data exploration, feature engineering, model development, validation, experimentation, deployment, monitoring, and continuous improvement.
• Partner closely with engineering, SRE, product, and operations teams to translate complex operational challenges into scalable, production-ready AI solutions.
• Influence architectural decisions related to data pipelines, model serving, observability integration, and AI platform utilization.
• Mentor and provide technical leadership to junior data scientists and engineers, promoting modeling rigor, experimentation discipline, and production best practices.

• Stay current with advancements in anomaly detection, time-series modeling, applied GenAI, and agentic AI systems—leading evaluation and adoption where appropriate.

  IC4 Career Level