Senior Data Scientist

Applicants must meet all qualifications and eligibility requirements by the closing date of the announcement including time-in-grade restrictions, specialized experience and/or education, as defined below. Time in Grade: Current federal employees applying for a promotion opportunity must meet the time in grade requirement of 52 weeks of service at the next lower grade level in the normal line of progression for the position being filled. For the GS-14 grade level: Applicants must have one year of specialized experience (equivalent to the GS-13 grade level) that demonstrates: · Using Python or R in support of data analytics projects. · Developing and executing approaches for analyzing, interpreting, visualizing, and verifying complex data and analyses. · Leading projects by establishing objectives, assigning responsibilities, reviewing deliverables, and offering expert guidance on analytical programming, statistical techniques, and data visualization strategies. · Developing oral and written presentations communicating complex and technical analyses to inform, influence, and persuade a variety of audiences. 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 and can provide valuable training and experience that translates directly to paid employment. You will receive credit for all qualifying experience, including volunteer experience.

Perform data cleaning and visualization.
Perform trend analysis and algorithm development.
Present briefings to senior leadership.
Develop new prescriptive analytics used in decision making.
Present alternate solutions to analytics and future concepts.
Develop statistical and machine learning models.
Analyze outputs from analyses.
Lead the design for data enhancement.
Recommend process improvement and design solutions to correct system errors.
Perform quality checks for statistical inference.
Interpret policy and regulatory requirements, provide education on new approaches to data analysis, and explain data principles.
Communicate in writing to internal and external individuals.
Select relevant sources of information.
Develop recommendations for data collection activities and data integration as well as usage and control policies.
Determine data used to support cases and develop hypotheses for testing.
Provide business metrics for assigned projects.
Maintain knowledge of science based analytical processes.