Data Scientist 3

Gormat • Annapolis Junction, MD

5d

About The Position

Gormat is seeking a Data Scientist who is familiar with Agentic AI, RAG, and Machine Learning model training in addition to the customer's enterprise data processing pipeline. You will be working to integrate AI into malware detection and malware analysis capabilities. The Level 3 Data Scientist shall possess the following capabilities: 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 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 DOD 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 DOD collection, processing, storage and analytic capabilities and limitations. AI/ML experience combined with CNE familiarity. Support the CNO community. Assess LLMs for effectiveness and applicability to CNE capability integration. Math and statistics background. Data normalization and visualization. Python required.

Requirements

• Bachelor's Degree with 10 years of relevant experience.
• Associates degree with 12 years of relevant experience.
• Bachelor's Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (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, or life) may be considered if it included a concentration of coursework (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).
• 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.
• Python required.
• TS/SCI with polygraph is required.

Nice To Haves

• AI/ML experience combined with CNE familiarity.
• Math and statistics background.
• Experience in more than one area is strongly preferred.

Responsibilities

• 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.
• Develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets.
• Translate practical mission needs and analytic questions related to large datasets into technical requirements.
• 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.
• Support the CNO community.
• Assess LLMs for effectiveness and applicability to CNE capability integration.
• Data normalization and visualization.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level