Data Scientist 3

Gormat

130d

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

• Familiarity with Agentic AI, RAG, and Machine Learning model training.
• Familiarity with customer's enterprise data processing pipeline.
• Foundations in Mathematics, Computation, and Statistics.
• Data management and curation skills.
• Data description and visualization skills.
• Workflow and reproducibility skills.
• Data modeling and assessment skills.
• Domain-specific considerations.
• AI/ML experience.
• CNE familiarity.
• Support the CNO community.
• Math and statistics background.
• Data normalization and visualization skills.
• Python required.
• Bachelor's Degree with 10 years of relevant experience OR Associates degree with 12 years of relevant experience.
• Relevant experience 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.
• TS/SCI with polygraph is required.

Nice To Haves

• Experience in more than one area of machine learning, data science, advanced analytical algorithms, programming, statistical analysis, data management, data mining, data modeling and assessment, artificial intelligence, and/or software engineering is strongly preferred.

Responsibilities

• Integrate AI into malware detection and malware analysis capabilities.
• 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 by maintaining awareness of the constantly shifting DOD collection, processing, storage and analytic capabilities and limitations.
• Assess LLMs for effectiveness and applicability to CNE capability integration.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1-10 employees