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Data Scientist

Summary

Data Scientist analyzes DHS data to detect fraud and inefficiencies, building dashboards and models in Python/R and Power BI/Tableau.

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 is an open continuous announcement with two cutoff Dates: 08/19/2026 09/02/2026 This position is in the 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, process, 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-11/12/13 include: Extracting, cleaning, transforming, and organizing structured and semi-structured data (e.g., resolving inconsistencies, handling missing values, normalizing fields) to prepare datasets for analysis. Applying descriptive statistics (e.g., counts, means, medians, standard deviations), inferential techniques (e.g., t-tests, chi-square tests) libraries or functions (e.g. in R or Python), or more advanced techniques (e.g., machine learning models, risk modeling) to address stakeholder questions and identify patterns, anomalies, or relationships in the data. Building and updating dashboards, reports, and visualizations in tools such as Power BI, Tableau, or similar platforms. Automating routine manual tasks (e.g., parsing large data files, merging files, recurring data quality checks, scheduled refreshes) to improve efficiency and reduce errors. Participating in discussions with auditors, investigators, inspectors, and other stakeholders to clarify data needs, understand business rules, refine analytical questions and translate stakeholder questions into concrete data tasks.

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