Data Scientist, CG-1560-15

Qualifying experience may be obtained in the private or public sector. 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.  Additional qualifications information can be found here.

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 above, plus additional education or appropriate experience.

YOU MUST PROVIDE YOUR TRANSCRIPT(S) (unofficial copy is acceptable)

In addition to the Basic Requirement, listed above, Applicant must also meet the following Specialized Experience:

To qualify, applicants must have completed at least one year of specialized experience equivalent to at least the 14 grade level or above in the Federal service.  Specialized experience is defined as experience in at least three of the following areas: (1) conducting analyses using machine learning algorithms including at least one of the following: neural networks, random forest, boosting, Naïve Bayes, K nearest neighbors, support vector machines, or K-means clustering; (2) performing Natural Language Processing using at least one of the following: transformer models (such as BERT) or local large language models; (3) conducting analyses using large datasets with millions of observations; and (4) writing code to conduct economic or statistical or machine-learning analyses using at least one of the following languages or technologies: Python, R, or Spark.