Description

The Stanford Synchrotron Radiation Lightsource (SSRL) at SLAC National Accelerator Laboratory, a national scientific user facility, seeks a Head of Data, Compute, Automation, and AI (DCAA) to lead a new facility-wide effort supporting operations and research across both experimental stations and accelerator systems. SSRL supports a broad range of scientific user programs in chemistry, materials science, environmental science, structural biology, and crystallography through advanced X-ray techniques and operation of a diverse set of beamlines based on the SPEAR3 storage ring.

The position requires demonstrated experience leading multidisciplinary teams and delivering complex, facility-scale technical initiatives in scientific or data-intensive environments.

The DCAA effort will focus on strengthening shared capabilities across data handling, compute access, and workflow integration, building on existing expertise across both experimental and accelerator environments. The objective is to improve consistency, usability, and efficiency in how data, analysis, and workflows are handled in practice, while enabling more immediate feedback during experiments and supporting increasingly complex and data-intensive scientific workflows, including more adaptive and feedback-driven experimental approaches.

The DCAA Head will build, mentor, and lead a high-performing multidisciplinary team working in close collaboration with SSRL staff scientists, engineers, technical staff, and computing teams. The role focuses on identifying common needs across techniques and systems and translating them into practical, reusable solutions that reduce duplicated effort while remaining flexible enough to support science-, technique-, and system-specific requirements.

The position includes setting priorities and guiding technical direction in close coordination with SSRL division leadership to ensure alignment with facility-wide goals and scientific programs. The DCAA Head will serve as a central coordination point for data, compute, automation, and AI-related activities across SSRL, working across organizational boundaries.

The role will also serve as a visible representative of SSRL in interactions with SLAC leadership and DOE-level data and computing initiatives, including ASCR and emerging national platforms such as the American Science Cloud, ensuring strong engagement and leadership presence in these efforts.

This is an SSRL-level leadership role with direct impact on experimental workflows, accelerator data utilization, and the overall effectiveness of SSRL operations.

Your specific responsibilities include:

• Lead and coordinate the development and execution of a facility-wide approach to data, compute, automation, and workflow integration.
• Build, mentor, and manage a multidisciplinary team spanning data architecture, software engineering, controls/automation, and compute integration.
• Guide the development and deployment of shared solutions for metadata capture, data organization, workflow orchestration, and analysis tools that support both experimental stations and accelerator use cases.
• Work in close coordination with SSRL division directors and facility staff to align priorities, scope efforts, and ensure that DCAA activities support scientific and operational objectives.
• Establish and maintain reliable and supported pathways for data movement, storage, and analysis, while serving as a visible representative of SSRL in DOE computing initiatives, including ASCR and national platforms such as the American Science Cloud, ensuring strong engagement and leadership presence in these efforts.
• Oversee the development of automation, first-pass analysis workflows, and real-time feedback capabilities that improve experimental efficiency and data accessibility across the facility.

To be successful in this position you will bring:

• An advanced degree (minimum Master's, PhD preferred) in a relevant field such as physics, chemistry, materials science, computer science, engineering, or a related discipline.
• Demonstrated experience leading technical teams and delivering complex projects in scientific, engineering, or data-intensive environments.
• A strong background in either scientific research or computational/data systems, with the ability to operate effectively at the interface between experimental workflows and computing infrastructure.
• Experience working in, or closely with, experimental or facility environments, with an understanding of operational constraints, user-facing workflows, and the need for reliable, production-level solutions.
• Proven ability to translate diverse and sometimes competing requirements into practical, scalable approaches that can be adopted across multiple teams and use cases.
• Experience coordinating efforts across organizational boundaries and working effectively with scientists, engineers, and computing professionals at different levels of expertise.
• Strong communication, collaboration and leadership skills, including the ability to guide technical direction, build alignment across stakeholders, and represent activities at the institutional or national level.

Requirements

To be successful in this position you will bring:

• An advanced degree (minimum Master's, PhD preferred) in a relevant field such as physics, chemistry, materials science, computer science, engineering, or a related discipline.
• Demonstrated experience leading technical teams and delivering complex projects in scientific, engineering, or data-intensive environments.
• A strong background in either scientific research or computational/data systems, with the ability to operate effectively at the interface between experimental workflows and computing infrastructure.
• Experience working in, or closely with, experimental or facility environments, with an understanding of operational constraints, user-facing workflows, and the need for reliable, production-level solutions.
• Proven ability to translate diverse and sometimes competing requirements into practical, scalable approaches that can be adopted across multiple teams and use cases.
• Experience coordinating efforts across organizational boundaries and working effectively with scientists, engineers, and computing professionals at different levels of expertise.
• Strong communication, collaboration and leadership skills, including the ability to guide technical direction, build alignment across stakeholders, and represent activities at the institutional or national level.