Environmental Data Scientist (AI/ML)

AIS Inc•San Diego, CA

22h•Onsite

About The Position

A.I.S., Inc. (AIS) is seeking an Environmental Data Scientist (AI/ML) to support the SWFSCs Ecosystem Science Division (ESD). The role involves developing and operationalizing AI/ML workflows for processing and analyzing large-scale imagery datasets from various advanced technologies. The specialist will evaluate methods, develop data management systems, deploy models in cloud environments, and enhance the efficiency and scalability of analytical capabilities.

Requirements

Master's Degree in a related field plus two (2) years' experience
Demonstrated experience coding in Python, R, and/or C/C++
Experience developing automated workflows for data processing or analysis
Experience maintaining application software and data management tools
Must be able to work in the U.S.

Nice To Haves

Ph.D. may be substituted for master’s degree and 2 years of relevant experience
Experience developing or applying computer vision and machine learning models for ecological or environmental image analysis
Familiarity with Google Cloud Platform (GCP) services including virtual machine deployment and cloud storage
Experience with edge computing platforms or embedded systems for field data collection
Background in marine, environmental, or fisheries science contexts

Responsibilities

Develop new capabilities including data management systems and integrated workflows to increase efficiency of processing and preparing data for research projects and AI/ML model development
Explore various optical imagery datasets for the design, development, evaluation, and application of AI/ML capabilities, edge computing, and automated analytic workflows
Develop or adapt software for optical imagery processing and web-based dashboards using Python and/or C/C++
Deploy models using virtual machines (VMs) in the Google Cloud Platform
Execute technical research projects, track progress, and present technical material in a variety of forums including weekly team meetings, conferences, working groups, and program reviews
Create standard operating procedures (SOPs) for implementing AI/ML pipelines and instruct additional staff on their use
Submit monthly progress reports reflecting the status and progress of all tasks