Applied Ai Data Scientist - Jchi

This position has the option of working a hybrid (preferred) or remote schedule. DESCRIPTION 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 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 ability to work 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. Familiarity with cloud‑based data science platforms such as AWS and modern ML tooling. Proficient in Python, R, SQL, and related tools, with solid skills in model evaluation, validation, and performance optimization. Familiarity with enterprise health system environments, electronic health records, data integration, cloud platforms, and awareness of regulatory and privacy considerations in healthcare AI. Strong collaboration skills across clinical, operational, and technical stakeholders, contributing within teams focusing on innovation, rigor, and patient safety. MINIMUM QUALIFICATIONS Seven (7) years of related experience, education/training, OR a Bachelor's degree in a related area plus three (3) years of related experience/training. Intermediate knowledge of HPC / data science / CI. Advanced skills and demonstrated experience with HPC hardware and software performance analysis, research, design, modification, implementation, and deployment of HPC or data science or CI applications and tools. Regular interface with management. Contribution of research and technical content to grant proposals. Effective communication and interpersonal skills, including the ability to communicate technical information to both technical and non‑technical personnel at multiple levels in the organization and to external research and education audiences. Proven ability to independently resolve broad computing, data, or CI problems using introductory and/or intermediate principles. Self‑motivation and ability to work independently and as part of a team, learning effectively and meeting deadlines. Experience working in a complex computing/data/CI environment encompassing HPC, data science infrastructure and tools/software, and diverse domain science applications. Proven ability to work on multiple concurrent projects. Ability to understand research computing/data/CI needs, map use cases to requirements, and develop and implement solutions that meet those requirements. Broad experience in optimizing, benchmarking, HPC performance and power modeling, and analyzing hardware, software, and applications for HPC/data/CI. Effective collaboration with all levels of staff; technical, students, faculty, and administrators. PREFERRED QUALIFICATIONS 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, 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 translating 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 causal inference, experimental design, or decision‑focused modeling to support evaluation of real‑world interventions. Exposure to 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. 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: $97,200 - $182,000 (will be prorated if the appointment percentage is less than 100%) Hourly Equivalent: $46.55 - $87.16 Factors in determining the appropriate compensation for a role include experience, skills, knowledge, abilities, education, licensure and certifications, and other business and organizational needs. The Hiring Pay Scale referenced in the job posting is the budgeted salary or hourly range that the University reasonably expects to pay for this position. The Annual Full Pay Range may be broader than what the University anticipates to pay for this position, based on internal equity, budget, and collective bargaining agreements (when applicable). #J-18808-Ljbffr