As a Supervisory Data Scientist, you will perform the following duties:
• Direct the activities of multiple teams that are developing and implementing advanced computational models to simulate and forecast disease transmission processes and estimate key epidemic parameters from data
• Establish vision, goals, priorities, and performance standards of the organization
• Provide expert consultation and advice to Division and Center Directors in computational epidemiology and the application of sound statistical and epidemiological principles in the analysis of public health data
• Collaborate with leaders and scientists from CIOs across CDC to integrate CFA's modeling and simulation activities into regular practice
• Demonstrate expertise and attention to detail while leading high-profile modeling projects with real-world impacts
• Promote the use of state-of-the-art technology, enterprise-grade software development practices, and advanced modeling workflows that embrace open science while ensuring the scientific integrity, reproducibility, and validity of the outputs

Basic Qualifications:

A degree in 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
Have a 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.
Required Specialized Experience:

Applicants must have at least one year of specialized experience at or equivalent to the GS-14 in the Federal service as defined in the next paragraph.

Specialized experience is experience which is directly related to the position which has equipped the applicant with the particular knowledge, skills and abilities (KSAs) to successfully perform the duties of the position to include experience:
• Developing and interpreting dynamical models for disease transmission across a range of deterministic and stochastic methodologies and of varying complexity
• Leading a team, i.e., modelers or quantitative analysts, to inform decisions while maintaining the scientific integrity of the work
• Communicating complex scientific and mathematical information to a wide range of technical and non-technical audiences.