Data Scientist

About this role

Who May Apply: Only applicants who meet one of the employment authority categories below are eligible to apply for this job. You will be asked to identify which category or categories you meet, and to provide documents which prove you meet the category or categories you selected. See Proof of Eligibility for an extensive list of document requirements for all employment authorities. Current Department of Army Civilian Employees If you are not a current Army employee and would like to be considered for a Temporary Appointment NTE 1 Year position, please apply under Announcement MCGT-26-13057041-DHA.

In order to qualify, you must meet the experience requirements described below. 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). You will receive credit for all qualifying experience, including volunteer experience.

Your resume must clearly describe your relevant experience; if qualifying based on education, your transcripts will be required as part of your application. Additional information about transcripts is in this document. Basic Requirements for Data Scientist GS-1560-14 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. In addition to meeting the basic requirement above, to qualify for this position you must also meet the qualification requirements listed below: Specialized Experience One year of specialized experience which includes 1. Architecting and deploying end-to-end machine learning models, statistical solutions, and advanced analytics pipelines to solve complex operational challenges; 2.

Automating enterprise data ingestion, transformation, and analytical workflows using modern programming languages (e.g., Python, R), APIs, and cloud/hybrid SDKs; 3. Designing and delivering executive-level interactive visualizations and decision-support products to communicate analytical insights to senior leadership; 4. Leading data initiatives, mentoring technical teams, or executing data literacy programs to drive organizational data-driven decision-making; and 5.