This position is located in the Division of Enforcement, Office of Federal Contract Compliance Programs (OFCCP). The purpose of the Office of Federal Contract Compliance Programs (OFCCP) is to enforce, for the benefit of job seekers and wage earners, the contractual promise of affirmative action and equal employment opportunity required of those who do business with the Federal Government. This position is inside the bargaining unit.

Duties

Major Duties: Performs a range of activities on assigned projects, with focus on work involving data manipulations and data mining on information present in the data. Obtains, processes, and mines complex data for patterns and clusters in support of agency goals and objectives. Creates and refines analytical models, write algorithms, and transform data into knowledge (e.g., dashboards) that guide data-informed decision-making and insights. Supports the implementation and maintenance of business intelligence and analysis solutions by identifying and gathering data needed; processing and transforming data into needed formats; developing processes for analysis; applying various tools and techniques to produce finding and insights from structured and unstructured data; and communicating the data and analytic results across the organization. Conducts research and statistical analysis on available data and presents findings in the most relevant format, including text, graphs, tables, and models. Evaluates limitations and effectiveness of statistical programs/applications. Alters instructions, methodologies, and/or guidelines as necessary to achieve program objectives. Provides advice and guidance for leadership and program officials to define data requirements for structured and unstructured data sources to better deliver integrated data to use in a variety of analytical software tools including but not limited to statistical analysis, geospatial software, visualization software, and natural language processing software. Uses analytic tools to combine, analyze, and interpret very large and complex datasets to perform exploratory analyses and develop data-informed organizational solutions. Conducts evaluations and assessments of the effectiveness of data science proposals and solutions to provide findings, conclusions, and recommendations to technical and program staff. Participates in designing, implementing, and evaluating analytical applications, projects, and studies, ranging from simple to very complex, applying a combination of analytical skills including but not limited to descriptive analysis, computational methods, natural language processing (NLP), and machine learning (ML) methods. Performs optimal and appropriate analysis on large and very large data sets. Prepares reports on in-depth data analyses using machine learning, statistics, and data visualization skills. Presents and distributes finds appropriately. Analyzes and explores a variety of large data sources to provide data-informed insights and actionable recommendations. Conducts statistical analysis to make appropriately rigorous and informed projections, data-informed decisions, and recommendations based on findings. Conducts hypothesis testing using appropriate and rigorous statistical methodology. Develops programs-relevant insights from large data sets. Effectively communications analytical results and technical methods and constraints to both technical and non-technical audiences, especially in cases where data may limit rigor or analytical methods produce uncertain results. Develops and maintains appropriate methods to track and document data, analysis, and tools used in professional work, including but not limited to documentation describing the development, operation, and maintenance of data transfer, manipulation, analytical modeling, data quality assessment, and actions taken to ensure appropriate data security and disclosure avoidance. Utilizes open-source languages such as R or Python to manage data, assess fitness for analysis, and prepare for and apply quantitative techniques (e.g., descriptive analysis, sampling, correlation, regression, and prediction) using both traditional inferential statistics and their machine learning analogs. Coordinates and participates in meetings and other activities with stakeholders to determine effective and efficient methods to acquire and ingest external data. Identifies trends and patterns in data through statistics, clustering, and data mining in support of data analytic efforts, including proactive data review and modeling. Conducts comprehensive evaluations and assessments of the proposed methodology, suitability of data, and anticipated effectiveness of data science proposals and solutions. Communicates findings, conclusions, and recommendations to technical and program staff. Supports the development of and adheres to guidelines for data science activities and applications. Ensures implementation of, and compliance with, data ethics and data science-related regulations, policies, and other guidelines issued by Federal, Departmental, and standard-setting bodies. Performs other duties as assigned.

Requirements

• Must be at least 16 years old.
• Must be a U.S. Citizen.
• Candidate required to obtain the necessary security/investigation level.
• Requires a probationary period if the requirement has not been met.

Qualifications

IN DESCRIBING YOUR EXPERIENCE, PLEASE BE CLEAR AND SPECIFIC. WE WILL NOT MAKE ASSUMPTIONS REGARDING YOUR EXPERIENCE. POSITION TITLES ALONE CANNOT BE USED TO DETERMINE IF YOU ARE QUALIFIED. PLEASE LIST DATES IN MM/DD/YYYY FORMAT. THIS IS IMPERATIVE IN DETERMINING IF WHETHER YOU HAVE AT LEAST ONE YEAR OF CREDITABLE SPECIALIZED EXPERIENCE. DATES WILL NOT BE ASSUMED. THIS POSITION HAS A BASIC REQUIREMENT. ALL APPLICANTS MUST MEET THIS REQUIREMENT IN ORDER TO BE CONSIDERED. Education: A. 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 B. 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. In addition to meeting the basic entry qualification requirements, applicants must demonstrate the specialized experience in their resume. Specialized Experience is the experience that equipped the applicant with the particular knowledge, skills, and abilities (KSA's) to perform the duties of the position successfully, and that is typically in or related to the position to be filled. To be creditable, specialized experience must have been equivalent to at least the next lower grade level. GS-12: Applicants must have 52 weeks of specialized experience equivalent to at least the GS-11. Qualifying specialized experience is experience that equipped the applicant with the particular knowledge, skills, and abilities to perform the duties of the position. Examples of qualifying specialized experience include: - Compiling, processing, and mining structured and unstructured datasets for patterns, trends, and clusters. - Transforming data into knowledge/visualization (e.g. dashboards, graphs, charts, tables, and models) that guide data-driven decision-making and insights. - Drafting written analysis that summarizes important trends resulting from quantitative and qualitative data compilation and analysis.

Education

Any applicant falsely claiming an academic degree from an accredited school will be subject to actions ranging from disqualification from federal employment to removal from federal service.

If your education was completed at a foreign college or university, you must show comparability to education received in accredited educational institutions in the United States and comparability to applicable minimum course work requirements for this position.

Click Evaluation of Foreign Education for more information.

Contacts

• Address Office of Federal Contract Compliance Programs 200 Constitution Avenue NW Washington, DC 20210 US
• Name: Nekeysha Garcia
• Phone: 404-302-5863
• Email: [email protected]