Interdisciplinary

You may qualify at the GS-14, if you fulfill the following qualifications: A.

One year of specialized experience equivalent to the GS-13 grade level in the Federal service: Designing and implementing complex data models, machine learning algorithms, and statistical analyses Working with large, complex, multi-source datasets (structured and unstructured) Using statistical analysis tools such as SAS, Python, R, SQL, and enterprise analytics platforms Leading the design and implementation of advanced statistical and machine learning models to address enterprise-level challenges.

Led the architecture and optimization of enterprise relational database environments supporting millions of records across multiple systems. Advising senior leadership on data-driven strategies Overseeing large-scale analytics projects Psychology Series, 0180 Basic Requirements Degree: major or equivalent in psychology.

Operations Research Series, 1515 Basic Requirements: Degree: in operations research; or at least 24 semester hours in a combination of operations research, mathematics, probability, statistics, mathematical logic, science, or subject-matter courses requiring substantial competence in college-level mathematics or statistics. At least 3 of the 24 semester hours must have been in calculus.

Statistics Series, 1530 Basic Requirements: Degree: that included 15 semester hours in statistics (or in mathematics and statistics, provided at least 6 semester hours were in statistics), and 9 additional semester hours in one or more of the following: physical or biological sciences, medicine, education, or engineering; or in the social sciences including demography, history, economics, social welfare, geography, international relations, social or cultural anthropology, health sociology, political science, public administration, psychology, etc.

Credit toward meeting statistical course requirements should be given for courses in which 50 percent of the course content appears to be statistical methods, e.g., courses that included studies in research methods in psychology or economics such as tests and measurements or business cycles, or courses in methods of processing mass statistical data such as tabulating methods or electronic data processing, or Combination of education and experience -- courses as shown in A above, plus appropriate experience or additional education.

The experience should have included a full range of professional statistical work such as (a) sampling, (b) collecting, computing, and analyzing statistical data, and (c) applying statistical techniques such as measurement of central tendency, dispersion, skewness, sampling error, simple and multiple correlation, analysis of variance, and tests of significance. *NOTE: Failure to provide transcripts will result in you being rated ineligible for this position.

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, 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.

As a Interdisciplinary at the GS-0180/1515/1530-14 some of your typical work assignments may include: Performing data analysis across the data lifecycle (e.g., data collection, ingestion, storage, modeling, access, integration, analysis, and decision support) to ensure compliance with the organization's mission; Performing data collection and analysis utilizing analysis software (e.g., SPSS, R Studio, Tableau), coding languages (e.g., Python, Java, Scala), database structures, and/or queries using structured query language (SQL) to provide support transition and reintegration activities; Use programming language(s) as well as various applications to organize, manipulate, and derive meaning from big data (e.g., extremely large, disparate, unstructured, and/or otherwise complex datasets); Delivering presentations (e.g., briefings, training sessions, and consultations) regarding the progress and/or results of projects/studies; Executing complex relational database activities involving data integration, extraction, transformation, validation, and delivery across multiple systems, ensuring data integrity, compliance with enterprise governance standards, and timely dissemination for actionable information to stakeholders