Data Scientist 4 [D.26.0031]

Requires Top Secret/SCI with Full Scope Poly

Duties:

• 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.
• 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 collection, processing, storage and analytic capabilities and limitations.

Requirements:

• Need an established Al expert with demonstrable experience designing and implementing sophisticated Al/ML and data science
  solutions. Candidate must be familiar with design and implementation of Agentic Al systems and customized Retrieval Augmented Generation (RAG) capabilities.
• Candidate must possess deep knowledge and experience in Al solutions and an ability to assess, evaluate, and implement the correct ML models for specific purposes and optimized performance. They must have experience generating, evaluating, training and optimizing Machine Learning models (i.e. LLMs) for use in low-memory, low-resourced edge device environments.
• A Bachelor’s degree and 15 years of relevant experience. An Associate’s degree plus 17 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position.
• 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 a single or multiple domain areas, and/or software engineering.