Data Scientist

To qualify for this position, applicants must meet all requirements by the closing date of this job opportunity announcement, 06/22/2026. INDIVIDUAL OCCUPATIONAL REQUIREMENT (IOR) / BASIC REQUIREMENT: Degree: Mathematics, statistics, computer science, data science or field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position. or Combination of education and experience: Courses equivalent to a major field of study (30 semester hours) as shown in paragraph A above, plus additional education or appropriate experience. To qualify at the GS-13 grade level: In addition to basic requirements, applicants must have completed at least 1 full year (52 weeks) equivalent to the next lower grade level of GS-12, in a federal, state and/or private setting. Specialized experience for this position must include: (1) Experience using SQL querying language (such as Oracle SQL or PLSQL) to acquire data from relational databases for data science projects. (2) Experience using data mining techniques to design and execute data science projects. (3) Experience using data visualization tools (such as Microsoft Power BI or Tableau) for data visualization (graphs, tables, charts, etc.,) and end-user business intelligence. Examples of specialized experience for this position may include: Experience preparing and analyzing structured and unstructured datasets for exploration and evaluating data science centric models. Experience working with multiple data types and formats as a part of a data science project. Experience applying a range of analytic approaches, including (but not limited to) machine learning, text analytics, and natural language processing; graph theory, link analysis and optimization models; complex adaptive systems; and/or deep learning neural networks that are part of the exploration Experience coding in various programming languages (such as R, Python, SQL, or JAVA) to conduct various phases of data science projects. Experience creating and querying different datastores and architectures (such as Sybase, Oracle, and open-source databases) to work with various types of data as part of the data science project. Experience planning and executing a variety of data science and/or analytics projects. There is no educational substitution authorized for the grade level of GS-13. Volunteer Experience: 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; religions; spiritual; community; student; social). Volunteer work helps build critical competencies, knowledge, and skills and can provide valuable training and experience that translates directly to paid employment. You will receive credit for all qualifying experience, including volunteer experience. Full-Time vs. Part-Time Employment: Full-time employment is considered to be at least 35 hours per week. Part-time experience will be credited on a pro-rated basis; when including part-time employment in your resume you must specify the average hours worked per week. For more information on these qualification standards, please visit the United States Office of Personnel Management's website at http://://www.opm.gov/policy-data-oversight/classification-qualifications/general-schedule-qualification-standards/.

Conduct regression analysis, statistical analysis, modeling, machine learning, Natural Language Processing, time series forecasting, exploratory data analysis and data visualization.
Work in established and emerging data science tools such as Toad, SAS, R, Python, PowerBI, Tableau, and cloud analytics platforms. Conduct data extraction by using computer programming without preset specifications.
Responsible for identifying the methods, processes, algorithms, tools, and systems to extract and interpret findings from varied structured and unstructured datasets related to the data science lifecycle.
Complete work that involves the development of algorithms or tools to support data manipulation and processing as well as the use of data visualization techniques to articulate findings.
Utilize visualization tools, such as Oracle Business Intelligence Enterprise Edition (OBIEE), and Power BI.
Plan and conduct data science projects in support of workload forecasting; identifying Veteran, medical disability examinations, claims, and medical disability examinations.
Create and design dashboards and other data visualization reports to support MDEO leadership and external stakeholders. Support transition and migration of reporting platforms.
Monitor, track, and communicate changes and developments in data science research and technology. Report key advances and results from proof-of-concept testing to assigned project lead or supervisor.