Scientist Senior

SAIC • College Park, MD

3d

About The Position

SAIC is seeking a highly-skilled and experienced candidate to support the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) in the cutting-edge research, development, and operational transition of the Global Chemistry Analysis and Forecast System (GCAFS) aerosol data assimilation (DA) within the Unified Forecast System (UFS) framework. The candidate's work will focus on the scientific advancement and successful implementation of the GCAFS aerosol DA system. The ideal candidate will be an expert in the Joint Effort for Data Assimilation Integration (JEDI) framework, advanced computational science, and sophisticated data assimilation principles to enhance the performance, stability, and future development of the GCAFS system. Job Description: The candidate will perform their job duties to a high standard, working both independently and collaboratively, focusing on scientific developments that advance GCAFS within the JEDI framework and ensure a smooth transition to operation. SAIC accepts applications on an ongoing basis and there is no deadline. SAIC® is a premier Fortune 500® mission integrator focused on advancing the power of technology and innovation to serve and protect our world. Our robust portfolio of offerings across the defense, space, civilian and intelligence markets includes secure high-end solutions in mission IT, enterprise IT, engineering services and professional services. We integrate emerging technology, rapidly and securely, into mission critical operations that modernize and enable critical national imperatives. We are approximately 24,000 strong; driven by mission, united by purpose, and inspired by opportunities. SAIC is an Equal Opportunity Employer. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $7.5 billion. For more information, visit saic.com . For ongoing news, please visit our newsroom .

Requirements

• Expert knowledge of the physical and mathematical basis of geophysical modeling.
• Advanced knowledge and hands-on experience with the Joint Effort for Data Assimilation Integration (JEDI) framework.
• Experience with Python and associated libraries used for data analysis in geosciences.
• Advanced knowledge and experience of modern programming languages such as object-oriented FORTRAN and/or C++.
• Experience in model testing and evaluation and/or knowledge of verification principles.
• Experience working in a UNIX environment with advanced scripting languages
• Experience with high-performance computing (HPC) environments and technologies such as MPI and OpenMP.
• Ability to work independently and in a team environment on complex problems.
• Good oral and written communication skills in English.
• Bachelors and five (5) years or more experience; Masters and three (3) years or more experience; PhD and 0 years related experience

Nice To Haves

• A Master’s Degree or higher in atmospheric sciences, meteorology, physical oceanography, or related physical science with at least 8 years of experience in numerical prediction and data assimilation systems.
• Knowledge of DA application to aerosols and atmospheric chemistry
• Knowledge of aerosol modeling, model physics and emissions tuning.
• Knowledge of Earth observing systems used for aerosols and chemistry.
• Knowledge of modern software engineering practices (version control, integration, testing, and documentation).
• Experience in running numerical models on High Performing Computer (HPC) platforms using MPI, OpenMP, Slurm, LSF, etc.
• Demonstrated skill in performing tasks requiring organization and attention to detail.

Responsibilities

• Lead the advancement and tuning of the JEDI-based GCAFS aerosol DA system
• Design, develop, and maintain advanced DA algorithms in the GCAFS system, focusing on key aerosol species
• Conduct comprehensive, long-term cycling data assimilation experiments
• Advance the use of observations and quality control in GCAFS.
• Improve data assimilation and observation diagnostics for GCAFS.
• Rigorously evaluate GCAFS aerosol analysis and forecast results against independent data, including CAMS, MERRA2 reanalysis.
• Investigate and resolve GCAFS model and DA issues, using data analysis tools to identify the cause of issues.
• Advance global workflow for GCAFS, ensuring readiness for both retrospective and real-time experiments.
• Assist in the operational transition of the GCAFS aerosol DA system

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees