Data Scientist

The Office of Inspector General (OIG) is an independent office whose mission is to promote excellence, integrity, and accountability throughout the Department of Homeland Security (DHS). In our dynamic environment, the OIG conducts investigations, audits, evaluations, and inspections to enhance program effectiveness and efficiency and to detect and prevent waste, fraud, and mismanagement in DHS programs and operations.

This announcement is for the DHS-Wide Career Expo in Buffalo, NY. Applicant must register for the Career Expo to apply and be considered. https://www.dhs.gov/homeland-security-careers/expo This position is in Office of the Chief Data Officer (OCDO), Office of Innovation (INN), Office of Technology & Innovation (OTI), Office of the Inspector General (OIG), Department of Homeland Security (DHS). The incumbent will serve as a Data Scientist, responsible for utilizing scientific methodologies, processes, algorithms, and systems to extract insights from structured and unstructured data and to provide guidance for data-driven decision making.in support of OIG investigations, audits, and inspections. Typical assignments for GS-7 include: Extracting data from internal/external data sources and provided files using prescribed tools, scripts, or step-by-step procedures; performing basic cleaning tasks; and loading into approved environments. Calculating basic descriptive statistics (e.g., counts, totals, averages, percentages) using spreadsheets or standard software functions and running predefined statistical routines or notebook templates. Creating simple charts, graphs, and tables (e.g., bar charts, line charts, pie charts) in tools such as Excel or introductory business intelligence tools using prepared data. Running existing scripts (e.g., Python, R, Perl, VBScript) to import, transform, or export data according to written instructions. Typical assignments for GS-9 include: Extracting, cleaning, transforming, and organizing data from internal and external data sources using established tools, scripts, and queries. Applying descriptive statistics (e.g., counts, means, medians, standard deviations), simple inferential techniques (e.g., t-tests, chi-square tests) or standard libraries or built-in functions (e.g. regression, classification, clustering) to answer defined questions. Building and updating dashboards, reports, and visualizations in tools such as Power BI, Tableau, or similar platforms using prepared or team-approved data models. Automating routine manual tasks (e.g., parsing large data files, merging files, recurring data quality checks, scheduled refreshes) to improve efficiency and reduce errors.