Applied AI Data Scientist - JCHI - 138983

UC San Diego Health is on a journey to build and mature enterprise 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 applied AI efforts is grounded in the quadruple aim, using AI-enabled technologies to simultaneously 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, predictive, generative, and hybrid models, and increasingly automated interventions to demonstrably achieve impact at scale across the health system.

Central to this strategy is the Mission Control vision, which brings together real-time data and applied AI-driven decision support to provide system-wide insight and action across the care continuum, including population health. This initiative serves both as a delivery layer for targeted point solutions across the health system and as a centralized hub for system-level assessment, prediction, and coordinated action. The supporting technology landscape 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 position operates within the Jacobs Center for Health Innovation and is integrated into UC San Diego Health Information Services, sharing leadership, data, and infrastructure, while driving translational innovation and supporting enterprise operations at scale.

Position and Team:

UC San Diego Health is seeking a Data Scientist to work within the Joan & Irwin Jacobs Center for Health Innovation (JCHI) on all phases of AI model design and development. The role contributes to the full model lifecycle, including intake and review, data preparation, model development, evaluation, pre-deployment preparation, deployment, and post-deployment model monitoring and management. This includes applying statistical and machine learning methods, along with elements of experimental design and evaluation, to support models that drive measurable impact through real-world interventions.

The position operates within a multidisciplinary environment that includes cloud engineers, product managers, AI engineers, application developers, architects, and enterprise platform teams. The Data Scientist partners with senior data scientists and product managers to execute across the model lifecycle, contributing to technical development while supporting product strategy, prioritization, and stakeholder alignment. The position reports to the JCHI Co-Director, who provides functional leadership for data science and AI initiatives and works across JCHI and Information Services stakeholders to align priorities, capabilities, and delivery.

This role requires the ability to independently execute defined components of data science projects while contributing to larger, more complex initiatives. The role involves collaboration with clinical and operational stakeholders to help translate clinical questions into data science problems and deliver AI solutions that create value across the health system. This includes working with diverse data modalities (e.g., structured EHR data, time-series, and unstructured text) and applying established approaches, including predictive and generative models.

What We're Looking For:

The ideal candidate brings experience across the AI model lifecycle in healthcare or complex enterprise environments, with the ability to execute projects from data exploration through deployment and monitoring with appropriate guidance. Experience with healthcare EHR data, particularly Epic, is preferred, and candidates should be comfortable working with complex clinical datasets to develop models that support improvements in patient care and health system operations.

Experience developing AI models using traditional machine learning and exposure to large language models or hybrid approaches is desired, along with familiarity with cloud-based data science platforms such as AWS and modern ML tooling. Candidates should be proficient in Python, R, SQL, and related tools, and demonstrate solid skills in model evaluation, validation, and performance optimization.

Successful candidates will also demonstrate familiarity with enterprise health system environments, including electronic health records, healthcare data integration, and cloud platforms, as well as awareness of regulatory and privacy considerations in healthcare AI. The role requires the ability to collaborate effectively across clinical, operational, and technical stakeholders, and to contribute within teams operating in environments where innovation, rigor, and patient safety are critical.

• Seven (7) years of related experience, education/training, OR a Bachelor's degree in related area plus three (3) years of related experience/training. Related experience: data science, computational science, or a related quantitative discipline.

• Intermediate knowledge of HPC / data science / CI.

• 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.

• Demonstrated ability to regularly interface with management.

• Demonstrated ability to contribute research and technical content to grant proposals.

• Demonstrated effective communication and interpersonal skills. 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.

• Proven skills and experience in independently resolving broad computing / data / CI problems using introductory and / or intermediate principles.

• Self-motivated and works independently and as part of a team. Able to learn effectively and meet deadlines.

• Thorough 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.

• Proven ability to successfully work on multiple concurrent projects.

• Proven ability to understand research computing / data / CI needs, mapping use cases to requirements and how systems / software / infrastructure can support those needs and meet the requirements. Demonstrated ability to develop and implement such solutions.

• Demonstrated broad experience in one or more of the following: optimizing, benchmarking, HPC performance and power modeling, analyzing hardware, software, and applications for HPC / data / CI.

• Demonstrated experience and ability to collaborate effectively with all levels of staff; technical, students, faculty and administrators.

• Foundational data science skills across the AI model lifecycle (data prep, model development, evaluation, deployment support).

• Ability to manage individual workstreams within larger data science projects, with documentation and stakeholder communication.

• Experience working in cloud-based data science environments (AWS), including model training, experiment tracking, and deployment workflows.

• Proficiency in Python, R, SQL, Machine Learning (ML) frameworks, data visualization tools, and cloud computing platforms.

• Exposure to traditional ML, large language models, or hybrid AI approaches.

• Experience with model performance analysis, profiling, benchmarking, and basic optimization.

• Experience working with healthcare EHR data (e.g., Epic), including extracting, cleaning, and analyzing clinical datasets.

• Familiarity with population health, clinical operations, care coordination, or related healthcare domains.

• Familiarity with vendor-based data science and AI tools/platforms.

• Experience or interest in working within large academic medical centers or integrated delivery systems.

• Experience contributing to the translation of model outputs into actionable clinical or operational insights, and supporting evaluation through real-world data analysis and validation efforts.

• Experience applying advanced or state-of-the-art methods and contributing to their translation into production-ready solutions within complex, real-world environments.

• Exposure to or experience with causal inference, experimental design, or decision-focused modeling to support evaluation of real-world interventions.

• Exposure to or experience with validation approaches or study design in clinical or operational environments.

• Experience collaborating across data engineering, clinical, operational, and technical stakeholders to support the development and delivery of data science products in complex healthcare organizations.

• Operational familiarity with healthcare domains such as population health, clinical operations, or care coordination.

• 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.