CSS seeks highly qualified candidates for a Data Scientist position on an interdisciplinary research team of contract and federal employees. The integrated research team will support the NOAA National Centers for Coastal Ocean Science (NCCOS) Marine Spatial Ecology Division (), which is a nationally recognized scientific research program that conducts spatial ecological analysis, statistical modeling, ecological forecasting, and predictive mapping to support marine ecosystem management, conservation, and spatial planning. The candidate will be employed via a contract to work at the NOAA National Ocean Service in Beaufort, NC.
We seek candidates with demonstrated expertise using artificial intelligence/machine learning for image classification to fit a variety of advanced analytical and statistical models to marine ecological photogrammetry data. Models may include both physical and ecological aspects of marine and coastal ecosystems. Experience in computer science, remote sensing, statistics, or quantitative marine sciences is preferred. Successful candidates will help conceive and implement solutions to large, complex spatial and spatio-temporal challenges, including modeling of habitat usage, marine wildlife survey data, and/or physical, oceanographic, and geological aspects of marine habitat. Examples of potential projects include change detection in coral reefs and marine ecosystems structure in a variety of US jurisdictions.
Develop analytical capacity for spatially-explicit analyses of change based on large-area-imagery to address applied questions relevant to marine conservation, restoration, and management:
Develop an analytical and data management pipeline for quantitative analyses of large-area-imagery (e.g., Structure-from-Motion) to detect change in indicator metrics of marine benthic species (e.g., occurrence, abundance, size-frequency), habitat, and ecosystem properties;
Implement and/or develop machine learning algorithms for analyses, including algorithms for model selection, validation, skill assessment, and ground-truthing;
Develop and apply statistical and analytical approaches for Big Data integration, including data assimilation and multi-scale approaches for multiple different datastreams;
Automate data acquisition, analyses, accuracy assessment, and QA/QC.
Synthesize and interpret outputs and protocols in the context of applied management, conservation, and restoration scenarios.
Lead and contribute to peer-reviewed publications, presentations, and technical memoranda.
Provide analytic guidance to team members.
Travel to federal and state laboratories, academic institutions, and field missions as part of collaborative research projects (
Minimum of Master's degree or equivalent experience in Computer Science, Physical Oceanography, Applied Statistics, Ecology or similar highly quantitative field;
Must be able to pass a drug screen and Government background check.
High level of expertise executing spatially-explicit image classification with AI, Cloud processing and computing;
Experience with Big Data management and databases, especially for remote sensing and imagery;
Experience/familiarity with classification using commercial off-the-shelf AI solutions
Expertise executing statistical analyses in R and/or Python (a code sample may be requested to demonstrate proficiency);
Demonstrated ability to independently identify, analyze, and solve complex challenges in imagery analyses, working with large data sets and computationally complex tasks;
Demonstrated excellence in written and oral scientific communication skills;
Demonstrated experience working independently and with a team.
Ability to work effectively in a dynamic, fast-paced, team-oriented, multi-project, multi-disciplinary environment;
Non-U.S. citizens must possess current documentation authorizing employment in the United States and meet the minimum security requirements for access to federal facilities;
A National Agency Check and Inquiries (NACI) background check and fingerprinting will be required.
Experience with a range of spatial and statistical photogrammetry analyses techniques and products including machine learning, geostatistics, and corresponding model selection, skill assessment, and uncertainty characterization;
Ph.D. or additional research experience beyond Master's;
Knowledge of marine science and marine ecosystems;
Experience analyzing spatial marine ecological and/or habitat data;
Experience with operations of remote vessels (e.g., Autonomous Underwater Vehicles)
Experience with parallel and high-performance computing in cluster or cloud environments;
Record of academic publication;
Ability to go to sea aboard a research vessel or other field research
CSS is predominately a Federal Contractor and is subject to following the terms of Executive Orders. CSS requires all Employees (Direct, Indirect, government, state, and commercial), including employees working from home/remotely, to be fully vaccinated against COVID-19 or have an approved exemption. Exceptions to the COVID-19 vaccine requirements may be provided to individuals for religious beliefs or medical reasons. Requests for an exception must be submitted to the CSS HR Department.
CSS is an Equal Opportunity/Affirmative Action Employer who provides equal employment opportunities to all employees and applicants for employment without regards to race, color, religion, sex, gender identity, sexual orientation, pregnancy, national origin, age, disability, veteran status or genetic information. In addition to federal law requirements, CSS complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.