Data Scientist

Big Data, dataflows, Artificial Intelligence / Machine Learning (AI/ML) familiarity, Analytics in GME, Jupyter notebooks, and Spark.   Due to federal contract requirements, United States citizenship and an active TS/SCI security clearance and polygraph are required for the position.    

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.