HOW A SENIOR DATA SCIENTIST WILL MAKE AN IMPACT
Perform all responsibilities of the Data Scientist labor category.
Lead analytical projects of enterprise scope, including analyses that support Government-directed evaluations of program efficacy, Return on Investment (ROI), and preventative risk factors; select methods, define validation approaches, and ensure results are defensible and reproducible.
Review the analytical design, code, and results of Data Scientists for methodological soundness before delivery to the Government.
Instruct and mentor Data Scientists and other contract personnel in data science practices, tools, and the Government-furnished technology stack.
Develop and maintain analytical standards, business rules, model assumptions, validation procedures, and reproducible methodologies for the data science team, in coordination with the Data Science Advisor.
Design, develop, and automate enterprise dashboards, executive scorecards, Key Performance Indicators (KPIs), Measures of Performance (MOPs), and Measures of Effectiveness (MOEs) as requested by the Government.
Advise Government personnel on the appropriate use, limitations, and interpretation of analytical products and models.
WHAT YOU’LL NEED TO SUCCEED
EDUCATION
Bachelor’s degree in a quantitative discipline, including but not limited to statistics, biostatistics, mathematics, applied mathematics, data science, operations research, computer science, engineering, physics, economics or econometrics, epidemiology, geospatial science or geoinformatics, or psychology or another behavioral science with a quantitative-methods concentration, with documented coursework or professional application in statistics, programming, and quantitative analysis.
Minimum of five (5) years of professional experience applying data science or statistical methods to real-world problems; or a Master’s degree in one of the disciplines listed above and a minimum of three (3) years of such experience. Degree-program work does not count toward the Senior Data Scientist experience requirement.
Meet all qualifications of the Data Scientist labor category, with proficiency demonstrated through professional work experience rather than degree-program projects alone.
Advanced proficiency in Python or R, with disciplined software practices (modular code, testing, version control), and in SQL.