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 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 customer data holdings
Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist other 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 of 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)