This position is located within the Office of Planning, Analysis, and Risk Management (OPARM). OPARM is the FSIS centralized analytics group and tasked with integrating and analyzing relational data. The program also provides data visualization and analytics services to support FSIS decision-making, policy development, risk management, and strategic planning.
Applicants must meet all qualifications and eligibility requirements by the closing date of the announcement including specialized experience and/or education, as defined below. For the GS-14 grade level: Applicants must have one year of specialized experience (equivalent to the GS-13 grade level) that demonstrates: Using Python or R in support of data analytics projects. Developing and executing approaches for analyzing, interpreting, visualizing, and verifying complex data and analyses. Leading projects by establishing objectives, assigning responsibilities, reviewing deliverables, and offering expert guidance on analytical programming, statistical techniques, and data visualization strategies. Developing oral and written presentations communicating complex and technical analyses to inform, influence, and persuade a variety of audiences. Experience refers to paid and unpaid experience, including volunteer work done through National Service programs (e.g., Peace Corps, AmeriCorps) and other organizations (e.g., professional; philanthropic; religious; spiritual; community, student, social). Volunteer work helps build critical competencies and can provide valuable training and experience that translates directly to paid employment. You will receive credit for all qualifying experience, including volunteer experience.
Perform data cleaning and visualization.
Perform trend analysis and algorithm development.
Develop new prescriptive analytics used in decision making.
Present alternate solutions to analytics and future concepts.
Develop statistical models and machine learning models.
Analyzes outputs from analyses.
Leads the design for data enhancement.
Performs quality checks for statistical inference.
Provides education on new approaches to data analysis.
Develops recommendations for data collection activities and data integration.
Maintains knowledge of science based analytical processes.