Data Scientist - multiple levels - CLEARANCE and POLYGRAPH REQUIRED

Must be a US Citizen

Must have TS/SCI clearance w/ active polygraph

This position is open to multiple levels of years of experience; two (02) years within the last five (05) years must be directly related to the job you are applying for:

Level 04 requires a minimum seventeen (17) years of experience w/ Degree

Level 03 requires a minimum twelve (12) years of experience w/ Degree

Level 02 requires a minimum five (05) years of experience w/ Degree

Degree in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science. A degree in a related field (e.g., Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g., physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e., behavioral, social, and life) may be considered if it includes a concentration of coursework (typically 5 or more courses) in advanced mathematics (typically 300 level or higher; such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g., algorithms, programming, data structures, data mining, artificial intelligence). College-level Algebra or other math courses intended to meet a basic college level requirement, or upper-level math courses designated as elementary or basic do not count.

Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python) and skill in at least one mid-level language (e.g. C)), data mining, advanced statistical analysis (e.g. statistical foundations of machine learning, statistical approaches to missing data, time series), advanced mathematical foundations (e.g. numerical methods, graph theory), artificial intelligence, workflow and reproducibility, data management and curation, data modeling and assessment (e.g. model selection, evaluation, and sensitivity.

Employ some combination (2 or more) of the following areas: Foundations (Mathematical, Computational, Statistical); Data Processing (Data management and curation, data description and visualization, workflow, and reproducibility); Modeling, Inference, and Prediction (Data modeling and assessment, domain-specific considerations).

Devise strategies for extracting meaning and value from large datasets.

Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application specific knowledge.

Through analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent to Agency data holdings.

Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data.

Effectively communicate complex technical information to non-technical audiences.

Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting Agency collection, processing, storage and analytic capabilities and limitations.