Intel. Automation Engineer - Data Sci. & Analytics - 138850UC San Diego • United States
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Intel. Automation Engineer - Data Sci. & Analytics - 138850
UC San Diego
- United States
- United States
Über
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 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.
Hybrid or remote schedule is an option. This position has recently been accreted by UAW RP union and will be a part of that union moving forward.
Key Responsibilities
Design, build, configure, and optimize intelligent automation agents across enterprise platforms, including Notable Health, Epic Agent Factory, UiPath, and related orchestration systems.
Integrate data science and analytics capabilities into automation workflows, including model-driven decisioning, performance optimization, and pre- and post-intervention analytics.
Translate operational use cases into production-ready automation agents that integrate across multimodal environments.
Lead 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.
Collaborate closely 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.
Design and evaluate model-informed automation with appropriate human-in-the-loop controls, ensuring safe failure handling, auditability, and alignment with clinical and operational risk considerations.
Contribute to the organization’s automation governance framework and ensure rigorous, scalable, and high-performing deployment of AI-driven automation in clinical environments.
Continuously monitor, optimize, and measure real-world impact across complex, multi-platform workflows.
Minimum Qualifications
Nine (9) years of related experience, education/training, or a Bachelor's degree in a 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 in HPC hardware and software power and performance analysis, research, design, modification, implementation, and deployment of HPC or data science or CI applications and tools of large-scale scope.
Demonstrated ability to regularly and effectively communicate with unit-level management.
Ability to initiate research proposals and acquire funding.
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: 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, demonstrating research and technology project leadership and management skills.
In-depth experience assessing a broad spectrum of technical and research needs, establishing priorities, delegating and/or leading development of solutions to meet such needs.
Demonstrated advanced experience in one or more of: 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‑employment physical.
Pay Transparency Act Annual Full Pay Range: Unclassified - No data available (will be prorated if the appointment percentage is less than 100%).
Hourly Equivalent: Unclassified - No data available.
Equal Opportunity Employer The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.
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Sprachkenntnisse
- English
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