SBC SR DATA & AI SOLUTIONS ENG

Stanford Health Care•Stanford, CA

19d•$73 - $96

About The Position

The SBC Sr Data & AI Solutions Engineer designs and integrates artificial intelligence, predictive algorithms, and other machine-learning innovations into Stanford Blood Center operations to improve outcomes, optimize resources, and help meet blood transfusion and transplantation requirements. The role leads end-to-end ETL pipelines that unify data across databases, data marts, and the enterprise data warehouse (EDW), and enhances business intelligence capabilities to support data-driven decisions, optimize blood production and testing workflows, and monitor performance with proactive support. The engineer builds and maintains AI and ML solutions across clinical, laboratory, and operational domains, applying safety, ethical governance, and cost-effectiveness. The position develops software and front-end web solutions, uses Python data-science capabilities and language models, and delivers BI, ETL, analytics, forecasting, and simulation solutions. It also improves operational processes and leads implementation projects, mentors BI analysts, and takes on broader responsibilities as business needs evolve. The role requires compliance with governmental regulations and Stanford Health Care and Blood Center health and safety policies, along with strict protection of donor, patient, research-subject, and team-member information. The position may involve entering areas where others work with human blood, with potential for unanticipated exposure to bloodborne pathogens. The listed bloodborne-pathogen category is Category III (no exposure to blood, body fluids, or tissues) and Category I tasks that are not a condition of employment.

Requirements

Bachelor's degree in engineering, computer science, data science, or a related field, plus 4 years of experience with applied problems.
3+ years to 5 years of hands-on experience designing and developing software across the full lifecycle, AI, and BI solutions, and being accountable for a complex EDW environment with robust ETL processes.
Experience developing, evaluating, and implementing machine-learning and artificial-intelligence models, partnering across clinical and operational areas, and validating researcher-built and vendor-provided algorithms or AI solutions.
Advanced object-oriented architecture and optimization skills; proficiency in Python (Django) and Java; HTML and CSS skills; AI/ML deployment experience, including LLMs; and performance-tuning ability.
End-to-end BI lifecycle ownership, including design, development, and deployment of BI solutions; BI tool administration; ETL design and development; EDW architecture; and delivery of reporting and analytics.
Experience with advanced analytics, forecasting, predictive modeling, and simulation to optimize operations, and ability to communicate results to diverse stakeholders and management.
Proficiency with BI and data visualization tools such as Power BI, Tableau, SSRS, or SAP BusinessObjects; SQL expertise in Oracle and MS SQL Server (Management Studio); SSIS; Azure DevOps Server; patching and upgrades; and hardware-utilization monitoring.
Ability to apply Lean principles to BI workflows, problem-solving, and Gemba-style observation rounds, and to use simulation to analyze production systems and optimal production scenarios.
Ability to prioritize work independently, balance speed and accuracy, maintain quality and reliable outputs, and exercise sound judgment.
Ability to seek and embrace coaching and mentoring and to formulate effectively.
Comply with applicable health and safety requirements and maintain the confidentiality of sensitive information.

Nice To Haves

Master's degree in engineering, computer science, data science, or a related field.

Responsibilities

Apply software development and object-oriented programming skills to build applications and front-end web solutions.
Build, deploy, and maintain AI and machine-learning solutions across clinical, laboratory, and operational domains, upholding safety, ethical governance, and cost-effectiveness.
Use Python data-science capabilities and libraries, virtual environments, and REST APIs to deliver analytics, solve complex problems, and build scalable data pipelines and applications; provide technical support and troubleshooting.
Fine-tune, prompt-engineer, and deploy large and smaller language models to enhance analytics, product development, decision-making, and productivity.
Develop and enhance BI and ETL processes, including error handling, operational logging and alerting, scheduling, and job control.
Prepare specifications, designs, data models, and diagrams for database development; manage a common data model to harmonize relational databases; tune queries and optimize EDW and BI architecture; monitor performance proactively.
Apply advanced data analysis, forecasting, prediction, and simulation to project demand for services and blood products and optimize supply chains and resource allocation.
Identify operational improvements through productivity analysis, process engineering, and optimization, and lead projects to implement improvements using project-management and communication skills.
Take on broader responsibilities as business needs evolve and mentor BI analysts.
Comply with governmental regulations and Stanford Health Care and Blood Center health and safety policies and practices.
Protect the privacy, security, integrity, and confidentiality of donor, patient, research-subject, and team-member information in accordance with laws, policies, professional ethics, accreditation standards, and legal requirements.