Job Duties
Employ some combination (2 or more) of the following skill 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 other appropriate scientific methods, 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 customer 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 constantly shifting collection, processing, storage, and analytic capabilities and limitations.
Required Skills:
US Citizens Only.
Active TS/SCI Clearance and Polygraph required.
Information Assurance Certification may be required.
Minimum of three (3) years of relevant experience and a Bachelor's degree or five (5) years of relevant experience and an Associate's degree required.
Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science.
A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university.
Relevant experience must be two or more of the following:
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.
Software engineering.
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