Data Analysis Software Specialist
Princeton University's Cooperative Institute for Modeling the Earth System (CIMES) has an opening in the area of advanced data analysis and visualization software engineering at the rank of Professional Specialist. We are seeking a candidate to work at CIMES joining a new and exciting initiative in collaboration with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), in the Modeling Systems Division.
The candidate is expected to work with leading scientists at CIMES and GFDL, understanding their scientific requirements, and building systems enabling advanced analysis of a federated worldwide archive of climate data on the Earth System Grid Federation. The particular challenge is to enable very large scale data analysis in a distributed environment without infringing on scientific creativity and the ability to pose new scientific questions and execute new and creative analytic applications.
Candidate will join a dynamic team of scientists attached to one of the foremost climate research institutions in the world, and will have access to an extraordinary range of data and computing resources. Candidate must be able to work in a team environment that combines the collegiality of an academic setting with the focus of a mission agency, and be able to deliver results at short notice to meet deliverables for international collaborative science missions and conferences. Occasional travel to project meetings, site visits, and national and international conferences and workshops is expected.
Candidate must possess knowledge and understanding of science and a minimum of 8 years' experience in the domain of scientific analytics and visualization, with an in-depth knowledge of data formats specialized for Earth system science (e.g netCDF). Knowledge of modern scientific programming languages such as Python, interactive environments such as Jupyter, experience with parallel execution of software containers on cloud or supercomputers all highly desirable.
Curriculum vitae, a publication list, and contact information for three references, should be submitted online to https://www.princeton.edu/acad-positions/position/11881.
Princeton University is an Equal Opportunity/Affirmative Action employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law. This position is subject to the University's background check policy.