Associate Scientist - Data Analytics

Position overview:

Are you interested in a scientific career in big data analytics uncovering correlations and insights? We are seeking a passionate and talented individual with strong programming and analytical skills to help develop advanced methods for analyzing large volumes of scientific data at the Linac Coherent Light Source (LCLS).

LCLS is the world’s premier X-ray free-electron laser (FEL) elucidating atomic structures and dynamics at the femtosecond timescale. LCLS currently produces as much as 1GB/s of scientific data, and it is anticipated that this data volume will increase a thousand-fold with planned upgrades to the accelerator and detectors in order to support world-leading capabilities in FEL science and advance SLAC’s mission to address transformative science and technology solutions.

As a Project Scientist, you will be a member of the Controls and Data Systems Division (CDS) a multidisciplinary team of physicists, electronic engineers and software developers focused on developing software solutions to support scientific instrumentation, laser systems, controls, and data analysis and acquisition systems. The evolution of science and experimental techniques on these instruments, along with upgrades in the X-ray FEL source and optical lasers for LCLS-II, require regular improvements to the supporting software and hardware platforms.

See for more on LCLS and the unique capabilities of our instrument facilities.

Note: This position is posted at the Associate Staff Scientist level. The Associate Scientist position is a 3-to-5-year fixed term position for a recent graduate or post-doctoral fellow and is the entry level in the Staff Scientist career path. The Associate Scientist position is scheduled to be assessed after the 3 to 5-year period with the possibility of appointment to the Staff Scientist level.

Applicants should include a cover letter, a statement of research including a summary of accomplishments, a curriculum vitae, a list of publications, and names of five-to-eight references for future letters of recommendation with the application.

Your specific responsibilities include:

• Development and optimization of data analytics code for coherent diffractive imaging, serial crystallography, and other imaging techniques on exascale supercomputers (50 to 100 times faster than the fastest supercomputers available today).
• Develop machine-learning techniques for data processing/reduction.
• Disseminate research results through peer-reviewed publications and presentations at scientific conferences.
• Work on the development and commissioning of research efforts involving closed-loop experiment steering to coherent diffractive imaging, serial crystallography, and other imaging techniques
• Develop real-time and offline data analysis frameworks which can offload computing intensive tasks to Graphics Processing Units (GPUs).
• Contribute to all phases of the project development cycle for big-data analytics software systems intended to enable rapid data analysis while also increasing accessibility for users of the facility.
• Develop and advance a collaborative research portfolio in x-ray diffuse scattering in protein crystals and the development of algorithms and pipelines for analyzing data from structural biology experiments performed at Free-Electron Lasers (FELs).
• Develop software that provides a simple user-friendly interface to complex systems so that software tools can quickly adapt to rapidly changing experimental configurations.
• Additional opportunities may also include:
• Ensure project quality and timely completion of milestones.
• Research and develop the data reduction techniques and associated data quality monitoring most appropriate to diffuse scattering, serial crystallography, and other diffractive imaging techniques.
• Other duties may also be assigned.
• Supervise junior members of the team and coordinate meetings with external stakeholders for technical projects, funding opportunities, reports, and publications.
• Review code pull requests and manage CI/CD.
• Understand complex scientific ideas to provide software solutions.

To be successful in this position you will bring:

• Strong written communication skills to articulate priority research directions and research results to the wider scientific community.
• Large-scale software parallelization techniques, such as MPI, and the use of high-performance computing clusters, including for analyzing streaming data.
• Complex applied math libraries and algorithms for optimization routines, non-uniform Fourier transforms, machine learning, etc.
• Exceptional communication skills and ability to work well in a research and development team.
• Ph.D. in computer science, software engineering, physics or a related scientific computing field with demonstrated expertise and minimum of 2 years of experience in large software systems (as evidenced by publications/presentations).
• Advanced programming skills with Python, C and/or C++.
• Strong conceptual and problem-solving skills as well as the ability to identify and recommend solutions.
• GPU offloading.
• Excellent organizational skills and the ability to synthesize complex technical and scientific information.
• Experience in one or more of the following is preferred.
• Code revision systems, such as git.
• Single particle imaging, serial crystallography, diffuse scattering, tomography or related imaging techniques is a plus.

SLAC Employee Competencies:

• Effective Decisions: Uses job knowledge and solid judgment to make quality decisions in a timely manner.
• Dependability: Can be counted on to deliver results with a sense of personal responsibility for expected outcomes.
• Adaptability: Flexes as needed when change occurs, maintains an open outlook while adjusting and accommodating changes.
• Relationships: Builds relationships to foster trust, collaboration, and a positive climate to achieve common goals.
• Initiative: Pursues work and interactions proactively with optimism, positive energy, and motivation to move things forward.
• Self-Development: Pursues a variety of venues and opportunities to continue learning and developing.
• Communication: Ensures effective information flow to various audiences and creates and delivers clear, appropriate written, spoken, presented messages.

Physical requirements and working conditions:

• Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job.
• Given the nature of this position, SLAC will require onsite work.

Work Standards:

• Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide,
• As an organization that receives federal funding, SLAC and Stanford University have a COVID-19 vaccination requirement that will apply to all university employees, including those working remotely in the United States and applicable subcontractors.
• Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for environment, safety and security; communicates related concerns; uses and promotes safe behaviors based on training and lessons learned. Meets the applicable roles and responsibilities as described in the ESH Manual, Chapter 1—General Policy and Responsibilities:
• Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.

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• Classification Title: Associate Scientist
• Job code:1092 Grade K
• Duration: 3-year fixed term

The expected pay range for this position is $108000 to $163,000 per annum. SLAC National Accelerator Laboratory/Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.