Chief Data Scientist
Food Safety and Inspection ServiceThe position of Chief Data Scientist is in the Office of the Chief Information Officer, Food Safety and Inspection Service, U.S. Department of Agriculture (USDA). The Office of the Chief Information Officer (OCIO) has primary responsibility for information technology and information management for FSIS.Advises on scientific issues pertaining to the development, planning, coordination, and implementation of statistical analyses of food safety and public health related to the full range of FSIS program areas.Conducts or directs full and comprehensive data science lifecycle, data analyses and performs integration studies that require detailed knowledge of food safety and public health issues.Cultivates and maintains contacts in the public health, food safety, data science/analytics, and scientific community, and with professional organizations and academic institutions, in an effort to complement and expand FSIS programs.Serves as the liaison with nationally and internationally recognized authorities and organizations whose policies impact the agency’s data infrastructure, analytics, and activities in support of food safety and public health initiatives.Supports in coordinating, evaluating, and improving the development and implementation of data analytics and the life cycle process model to support policy development and scientific policies as it relates to food safety and food defense.Reviews and scientifically evaluates new and proposed policies, procedures, regulations, and legislation that impact data analytics integration and food defense and safety programs.Collaborates with other Agency officials to ensure that decisions made and actions taken are fully supported by scientific and credible data and respond to the interests and needs of other programs and organizations.
Opens the company's application page
Listed via
USAJobs
usajobs.gov
Similar roles
Design & Tech
Related reads from TCHNX
The Inference Economy: Why Running AI Models Just Got Cheaper Than Training Them
A seismic shift in AI economics is underway. As inference costs plummet and training expenses soar, the entire industry must rethink how it builds, deploys, and monetizes intelligent systems.
The Emergence of Small Language Models: Why Efficiency Is Overtaking Scale
As the AI industry confronts computational costs and environmental concerns, a new generation of compact models is proving that bigger isn't always better. Small language models are reshaping enterprise AI deployment.
Algorithmic Bias in Design Systems: Why Your AI-Generated UI Might Exclude Users
As AI tools increasingly generate interface components, they're embedding biases that systematically exclude users. Understanding how machine learning models inherit prejudice is essential for creating truly inclusive design systems.