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Data Scientist Skill Level 2 

Employ some combination (2 or more) of the following skill areas: 1. Foundations: (Mathematical,Computational, Statistical) 2. Data Processing: (Data management and curation, data description and visualization, workflow and reproducibility) 3. 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 in NSA/CSS 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 NSA/CSS collection, processing, storage and analytic capabilities and limitations.

A Bachelor's degree and 3 years of relevant experience. An Associate's degree plus 5 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position. (U) Degree must be 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 otherscience 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. (U) Note: A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university. (U) 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)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one area is strongly preferred.