Intel. Automation Engineer - Data Sci. & Analytics

Intel. Automation Engineer - Data Sci. & Analytics

UC San Diego Health is on a journey to build and mature enterprise intelligent automation and applied artificial intelligence capabilities that deliver meaningful, measurable impact at scale across the health system. This work reflects a sustained organizational commitment to developing these capabilities as a core part of how care is delivered and supported. The purpose of UCSDH's intelligent automation efforts is grounded in the quadruple aim, using AI-enabled technologies to expand access to care, improve clinical and operational outcomes, enhance quality and safety, and support a better experience for both patients and care teams.

A key focus is leveraging real-time data, automation, and AI-driven decisioning to reduce administrative burden, enable more efficient operations, and allow clinicians and staff to spend more time on direct patient care. Central to this strategy is the Mission Control vision, which brings together real-time data, automation, and applied AI to provide system-wide insight and coordinated action across the care continuum, including population health. This includes delivering targeted solutions across the health system while also establishing a centralized capability for system-level assessment, prediction, and action.

This work is being developed under an enterprise Intelligent Automation Center of Excellence (CoE) model, enabling coordinated design, governance, and scaling of automation and AI solutions across a multi-platform, multi-vendor ecosystem. The technology landscape supporting this work is intentionally dynamic, with an emphasis on identifying best-fit solutions over time while scaling a cohesive enterprise platform that integrates complementary tools and capabilities.

This role emerged from the Jacobs Center for Health Innovation and now operates within UC San Diego Health Information Services under shared leadership with JCHI, sharing data, infrastructure, and strategic direction while maintaining close ties to translational innovation and supporting enterprise operations at scale.

UC San Diego Health is seeking an Intelligent Automation Engineer (Data Science & Analytics) to design, build, configure, and optimize intelligent automation agents across enterprise platforms including Notable Health, Epic Agent Factory, UiPath, and related orchestration systems. This role focuses on integrating data science and analytics capabilities into automation workflows, including model-driven decisioning, performance optimization, and pre- and post-intervention analytics. The engineer works closely with the Intelligent Automation Product Manager to translate operational use cases into production-ready automation agents that integrate across multimodal environments. This role focuses on building enterprise-scale, AI-enabled, cross-platform automation and decision systems that extend beyond traditional RPA to include agentic, event-driven, and model-informed automation operating across multiple platforms and environments.

The position operates within a multidisciplinary team that includes data scientists, product managers, cloud engineers, and enterprise platform specialists. The position reports to the JCHI Co-Director within UC San Diego Health Information Services.

This is a senior technical role that requires strong data science and engineering capabilities and the ability to independently lead complex, model-driven automation initiatives. The role involves close collaboration with clinical and operational stakeholders across Hospital operations, Care Navigation Hub, Revenue Cycle, Outpatient Departments, and Population Health to design and deploy automation solutions that drive measurable efficiency gains and sustained system-wide operational impact. The role includes designing and evaluating model-informed automation with appropriate human-in-the-loop controls, ensuring safe failure handling, auditability, and alignment with clinical and operational risk considerations. As part of the broader intelligent automation team, this role contributes to advancing the organization's automation governance framework and ensuring rigorous, scalable, and high-performing deployment of AI-driven automation in clinical environments, including continuous monitoring, performance optimization, and measurement of real-world impact across complex, multi-platform workflows.

What We're Looking For:

The ideal candidate brings strong experience in data science and applied AI, with demonstrated ability to develop, deploy, and optimize models within production automation environments in healthcare or similarly complex settings. Experience with intelligent automation platforms such as Notable Health, UiPath, Epic Agent Factory, or similar tools is preferred, and candidates should be comfortable integrating predictive, generative, and hybrid models into real-world workflows that drive operational and clinical interventions.

Candidates should demonstrate strong capabilities in data engineering, model development, and analytics, including proficiency in Python, R, and SQL, and experience working with cloud-based data platforms such as AWS. Experience building production-grade data pipelines, performing rigorous model evaluation, and conducting pre- and post-intervention analysis to measure real-world impact is essential. Familiarity with causal inference, experimental design, and performance monitoring of models in production environments is highly desired.

Successful candidates will also demonstrate familiarity with enterprise health system operations and healthcare data environments, including EHR data and clinical workflows. The role requires the ability to collaborate across clinical, operational, technical, and vendor stakeholders, and to contribute to data science strategy in environments where innovation, scientific rigor, performance, and patient safety must coexist.

Minimum Qualifications

• Nine (9) years of related experience, education/training, OR a Bachelor's degree in related area plus five (5) years of related experience/training. Related experience includes data science, machine learning engineering, AI model development, NLP, LLMs applied to enterprise automation use cases, and work with healthcare clinical datasets.
• Advanced knowledge of HPC / data science / CI.
• Highly advanced skills, and demonstrated experience associated with one or more of the following: HPC hardware and software power and performance analysis and research, design, modification, implementation and deployment of HPC or data science or CI applications and tools of large-scale scope.
• Demonstrated ability to regularly, effectively communicate with unit-level management.
• Demonstrated ability to initiate research proposals and acquire funding.
• Demonstrated ability to communicate technical information to technical and non-technical personnel at various levels in the organization and to external research and education audiences.
• In depth skills and experience in independently resolving complex computing / data / CI problems using introductory and / or intermediate principles.
• Self-motivated and works independently and as part of a team.
• Advanced experience working in a complex computing / data / CI environment encompassing all or some of the following: HPC, data science infrastructure and tools / software, and diverse domain science application base.
• In depth ability to successfully work and / or lead multiple concurrent projects. Demonstrated research and technology project leadership and management skills.
• In depth experience assessing a broad spectrum of technical and research needs and demands and establish priorities, delegate and / or lead development of solutions to meet such needs
• Demonstrated advanced experience in one or more of the following: optimizing, benchmarking, HPC performance and power modeling, analyzing hardware, software, and applications for HPC / data / CI.

Preferred Qualifications

• Strong data science and AI engineering discipline applied to intelligent automation: develop, optimize, and deploy AI-enabled capabilities using ML, NLP, LLMs, and hybrid AI across enterprise automation platforms.
• Demonstrated ability to lead complex AI R&D projects related to intelligent automation; serve as technical lead for data science workstreams within broader automation initiatives.
• Healthcare data science platform experience in AWS, including cloud-based model training, experiment tracking, and deployment infrastructure for AI capabilities embedded in automation agents.
• Advanced proficiency in Python, R, SQL, and ML frameworks (TensorFlow, PyTorch, scikit-learn) and NLP libraries applied to document processing, clinical text analysis, and conversational AI.
• Experience developing AI models using traditional ML, LLMs, and hybrid approaches with application to agent decision-making, automated clinical reasoning, and predictive workflow optimization.
• In-depth experience with benchmarking, profiling, and performance analysis of AI models and data science code applied to automation.
• Experience with Epic EHR data and clinical datasets to train and validate AI capabilities embedded in automation agents.
• Operational familiarity with care navigation, population health, clinical operations, or revenue cycle.
• Experience evaluating and integrating vendor-based AI/automation platforms.
• Experience operating within large academic medical centers or integrated delivery systems.
• Experience translating model outputs into production decisioning systems that trigger or inform automated workflows and interventions.
• Experience designing and evaluating interventions using causal inference, quasi-experimental methods, or A/B testing in real-world operational settings.
• Experience conducting pre/post implementation impact analysis, including measurement of operational, clinical, or financial outcomes at scale.
• Experience working with real-world, messy, and incomplete healthcare data, including strategies for data validation, bias mitigation, and robustness in production environments.
• Experience contributing to or supporting real-world evidence generation, including study design, evaluation frameworks, or publication-oriented work.

Special Conditions

• Must be able to work various hours and locations based on business needs.
• Employment is subject to a criminal background check and pre