Machine Learning Programmer/Analyst Needed
Integrated Statistics is looking for a Machine Learning Programmer/Analyst to support the National Oceanic and Atmospheric Administration (NOAA) National Marine Fisheries Service (NMFS) Northeast Fisheries Science Center (NEFSC). The NEFSC Protected Species Branch (PSB) mission includes evaluating threats to species and populations due to anthropogenic activities. The Machine Learning Programmer will be working on protected species bycatch, which is when protected animals get caught or entangled in fishing gear. Bycatch can negatively affect species such as dolphins, sea turtles, protected fish, and whales by harming animals, contributing to population declines, and impeding population recovery.
For this project, video data that was collected on commercial fishing vessels as part of a fisheries monitoring program is being reviewed so that bycatch events and species can be identified and further analyzed. Since such interactions are relatively rare events, these data are sparse and an automated review of the video can be a realistic and cost effective way to identify actual or probable protected species interactions.
The Machine Learning Programmer/Analyst will develop analyses of image data to inform bycatch estimates and assessments of fisheries impacts on protected species. The Analyst’s ultimate goal is to develop an algorithm automating the detection and identification of protected species visualized on video from gillnet, bottom trawl, and other types of fishing gear. The Machine Learning Programmer/Analyst will begin by curating and developing a protected species (bird, mammal, turtle) image library utilizing video and still image data sets. After that, the Machine Learning Programmer/Analyst will explore approaches to automating image analysis, developing algorithms in support of machine learning applications and annotated training data sets.
Qualifications and Skills
- Gaining familiarity with the data and processes, including image review, data analysis, image data set curation, preparing and processing data.Identifying, acquiring and/or generating, and characterizing data and annotations.
- Investigating and determining suitable machine learning technologies
and available open-source resources (software applications, coding, and
curated datasets) for a data-sparse context.
- Training and testing models.
- Developing machine learning based video analysis; creating a system and workflow for conducting the automated image analysis.
- Generating reports that use the image data to inform assessments of fisheries impacts on protected species.
- BS in Computer Science, Data Science, Mathematics, or a closely related field.
- Deep proficiency in at least one programming language relevant to machine learning and artificial intelligence (Python, R, Matlab, C++, VIAME, etc)
- Demonstrated skill and knowledge in applying artificial intelligence and machine learning techniques to image data.
- Experience in information extraction using machine learning, deep neural nets.
- Project development and management skills are desirable. Statistical programming skills are desirable.
- Coursework and/or exposure to biological data is desired, but not required. The skills needed are in programming, not biology.
- US citizenship and ability to comply with EO 14042 are required.
- Excellent written and oral communication skills.
- Track record of success in working remotely. Demonstrated ability to contribute timely deliverables and work collaboratively as part of a team.
The position is expected to be full-time for one year, the rate of pay depends on skills and experience, and the position is eligible for the benefits described on the Integrated Statistics website. The position will probably start remotely but with the goal of intermittent in-person work at the NEFSC lab in Woods Hole, MA.
Integrated Statistics is an equal opportunity employer and will not discriminate against any employee or applicant on the basis of age, color, disability, gender, national origin, race, religion, gender identity, sexual orientation, veteran status, or any classification protected by federal, state, or local law. Consistent with its obligations under federal law, Integrated Statistics is committed to taking affirmative action to employ and advance in employment qualified women, minorities, disabled individuals, special disabled veterans, veterans of the Vietnam era, and other eligible veterans. For assistance with accessibility of applications, posters, forms, and/or documents, please email the Integrated Statistics office.