Data Scientist/ Ocean Engineer

NOAA NCCOS seeks highly qualified candidates for a Data Scientist/ Ocean Engineer position on an interdisciplinary research team of contract and federal employees supporting the NOAA National Centers for Coastal Ocean Science (NCCOS) Marine Spatial Ecology Division (MSE) (http://coastalscience.noaa.gov/). NCCOS 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 through a labor contract supporting the NOAA National Ocean Service in Beaufort, NC.

• We seek candidates with demonstrated expertise in signal processing and creating macros or scripts to automate processing of data from remote or underwater sensors, and artificial intelligence / machine learning for computer vision and related tasks to characterize marine environments. MSE analyzes imagery to characterize underwater habitats, from the species and benthic substrates, to the larger-scale, ecological communities and ecosystems. Imagery may be collected from remote sensing platforms such as sensors on satellites, aerial drones, uncrewed surface vessels or autonomous underwater vehicles. Traditionally, MSE spatial analysts have manually interpreted and annotated the content of these imagery data sets to determine the types of benthic biological coverage (e.g., coral or seagrass) and substrate type (e.g., mud or sand), which is a time-intensive and subjective process. Automation and machine learning present scalable solutions for the MSE processing routines, specifically to 1) reduce human annotation effort, 2) reduce human-induced errors and bias, 3) enhance reproducibility for auditing, and 4) optimize pattern detection unapparent through manual analysis.
• The principal initial objective of this effort will be to focus on developing more efficient macros, scripting and AI-based techniques and workflows to assist in annotation and extraction of habitat information derived from underwater imagery and point clouds signal processing.

Core Responsibilities:

• Assist in marine mapping related projects by working with principal investigators (PIs) to understand their project objectives, and help develop solutions for automation at scale.
• Work with other NCCOS data scientists to facilitate deployment of automation and AI solutions on local machines or cloud environments.
• Implement and / or develop computer vision and machine learning algorithms for analyses, including algorithms for model selection, validation, skill assessment, and ground-truthing.
• Lead and contribute to peer-reviewed publications, presentations, and technical memoranda.
• Provide analytic and technical guidance to team members.
• Travel to federal and state laboratories, academic institutions, and field missions as part of collaborative research projects (<20% of time).

Required:

• Master's degree in the fields of Computer Science, Machine Learning, Information Technology, Computational Biology or Ecology, or similarly related.
• Minimum of 1-3 years of experience with developing and productionizing code in applied science, commercial, or business enterprises.
• Programming skills in Python, or any C-based language; knowledge of R, Javascript, or other scripting language would be an advantage.
• Experience working on computer vision applications including but not limited to: image classification, semantic segmentation, instance segmentation, object detection, and point cloud classification.
• Experience working with point cloud datasets derived from Structure-from-Motion, LiDAR, or sonar; manipulating data stored in file formats including in PLY, LAS, and LAZ.
• Experience working with open-source libraries to prepare data, fine-tune models, perform inference, and deploy models to cloud / production environments.
• Proficiency in version control tools such as Git, and GitHub is expected.
• Demonstrated excellence in written and oral scientific communication skills;
• Demonstrated experience working independently and with a team.
• Ability to work effectively in a 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.

Preferred:

• Experience working with environmental, ecological, or marine datasets.
• Experience working with geospatial data (optical, side-scan, synthetic aperture sonar, underwater lasers) obtained from marine-related vehicles including ROVs, AUV, ASVs, or previous work with autonomous driving vehicles.
• Experience with machine learning and computer vision libraries such as PyTorch, TensorFlow, Keras, OpenCV, Metashape, Open3D, and CloudCompare would be an advantage.
• Experience with writing code in Robotic Operating System (ROS) would be an advantage.
• Experience with GIS software including ArcGIS, QGIS, as well as geospatial coding libraries, would be beneficial
• Experience with containerization frameworks such as Docker, as well as virtual environments including Pip and Anaconda, and packaging Python projects.
• Experience in creating scripts and programs with GUIs.
• Familiarity with deploying code and models to Cloud providers such as Azure, and experience with Azure's Machine Learning Studio
• Record of academic publication;
• Ability to go to sea aboard a research vessel or other field research

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.