Associate Lab Director, Data & Computational Science

About the role

The Associate Laboratory Director, Data & Computational Science (ALD, Data and Computational Science) provides strategic and operational leadership for the High-Performance Data Facility (HPDF) and the Laboratory’s data and computational science enterprise.

Responsibilities

• Provide leadership and oversight to ensure the successful, reliable and safe operation of the Data & Computational Science capability with divisions in scientific computing and scientific data, and the data office.
• Collaborate with the HPDF Project Office to ensure support and execution of the HPDF DOE O 413.3b project and prepare for enduring operations of the HDPF capability and associate User Program.
• Develop mechanisms to support users to address critical research challenges, realize major scientific breakthroughs and develop cutting-edge particle detectors.
• Assess the changing Data & Computational Science needs of the physics user community.
• Maintain strong and respectful relationships with the major computing sponsor, Advanced Scientific Computing Research (ASCR).
• Lead division-wide talent strategies by championing leadership development, robust succession planning, and continuous performance coaching.
• Meet milestones for ongoing projects, including HPDF.
• Serve as a member of the Directorate, sharing responsibility for the excellence in science and technology, operational excellence, and community engagement at TJNAF.
• Work with Directorate and Lab Director’s Office to identify and implement other Laboratory research priorities, including thorough strategic use of LDRD (Laboratory-Directed Research and Development).
• Create and nurture an environment that promotes and embraces a multi-faceted workforce and champions the TJNAF values of impact, teamwork, safety, integrity and service.
• Provide Data & Computational Science perspectives on Laboratory-Level decisions and key Laboratory committees.
• Lead self-assessments, planning and tracking for Data & Computational Science elements associated with the Laboratory Agenda, Annual Laboratory Plan, and other performance tools.
• Implement management systems, operations, safety, compliance, and performance assessments across Data & Computational Science, using these systems to steward the capability.
• Lead - Supervisory - Management
• Perform line management of the Data & Computational Science leadership, including Scientific Computing, Scientific Data, and the Data Office.
• Identify key organizational staffing needs, recruit and develop S&T staff to be future leaders/mentors.
• Lead and manage, through direct report and matrixed staff, the Scientific Computing and Scientific Data divisions and the Data Office.

Requirements

• Required: 20 or more years International recognition and demonstrated accomplishments
• Required: 5 or more years Demonstrated executive management experience, including developing and leading large complex and multidisciplinary research programs and/or user facilities
• Required: Demonstrated experience of successfully developing, implementing, and executing scientific strategy with engagement from critical stakeholders
• Required: Experience communicating with key stakeholders, clients, program sponsors, internal staff, and the security/intelligence community
• Required: Proven commitment to safety and integrity
• Preferred: Track record of using data and computational science capabilities for scientific discovery

Qualifications

• Required: Ph.D.

Skills and Abilities

• Extensive knowledge of data and computational science capabilities for scientific discovery.
• Ability to lead and manage complex technical scope.
• Ability to establish priorities, assess competing resource demands and make decisions that optimize performance.
• Familiarity with the Department of Energy.
• Proficiency in financial analysis and forecasting.
• Strong analytical, writing, and speaking skills with the ability to organize, analyze, and implement complex topics and initiatives, with attention to detail.
• Capable of handling multiple competing priorities in a complex R&D environment.
• Commitment to safety, high standard of ethics, and integrity.