Data Scientist Level 3

Base-2 Solutions is looking for a Data Scientist Level 3.

Job Description

The Data Scientist will 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.

Position-Specific Skills and Experience

• Providing advanced discovery support utilizing machine learning, analytical prototyping, scripting, automation, data visualization, statistical analysis, and TechSIGINT tools. The solution needs to be adaptable to new tools and technologies as needed. Seeking outgoing candidates that work well in team or collaborative settings. Having any experience previously as an analyst, targeteer, or discovery skillset is highly preferred.

Education Required

• Bachelor's degree plus 10 years of relevant experience.
• Associate's degree plus 12 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position.
• Degrees 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 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.

Training/Certifications Required

• A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university.

Description of Relevant Skills and Experience

• 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 analysis), experience as a data scientist working to support single or multiple domain areas, and/or software engineering.
• Apply professional knowledge of computer science, mathematical and statistical theories, techniques, and methods to gather, analyze, design, and construct new processes for modling and interpreting trends, relationships, and correlations among or within data sets.
• Deliver data visualizations, analytics, and processing capabilities.

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