Head of Data, Compute, Automation & AI for Science

SLAC

Menlo Park (CA)

On-site

USD 230,000 - 276,000

Full time

10 days ago
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Benefits offered by this job

PTO
Health benefits
Flexible work options
Tuition assistance

Job summary

The SLAC National Accelerator Laboratory's SSRL seeks a Head of Data, Compute, Automation, and AI (DCAA) to lead a cross‑facility program spanning both experimental stations and accelerator systems.

You will build and mentor a multidisciplinary team, drive shared solutions for metadata, data organization, and workflow orchestration, and align work with SSRL and DOE computing goals to improve efficiency and data accessibility.

Qualifications

  • Advanced degree in a relevant field (Master's or PhD preferred).
  • Experience leading technical teams and delivering complex projects.
  • Experience in data-intensive environments and interfacing with experimental workflows.

Responsibilities

  • Lead and coordinate the facility-wide data, compute, automation, and workflow integration program.
  • Build, mentor, and manage a multidisciplinary team spanning data architecture, software engineering, controls/automation, and compute integration.
  • Develop shared solutions for metadata capture, data organization, workflow orchestration, and analysis tools.
  • Align SSRL priorities with division leadership and DOE computing initiatives.

Skills

Leadership
Data architecture
Software engineering
Automation
Workflow orchestration
Metadata management
Communication
Collaboration
Strategic thinking
Team management

Education

Master's degree or PhD in physics/CS/engineering or related

Job description

The SLAC National Accelerator Laboratory's SSRL seeks a Head of Data, Compute, Automation, and AI (DCAA) to lead a cross‑facility program spanning both experimental stations and accelerator systems.

You will build and mentor a multidisciplinary team, drive shared solutions for metadata, data organization, and workflow orchestration, and align work with SSRL and DOE computing goals to improve efficiency and data accessibility.

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