Principal Computer Scientist

Location: Reston, VA

Department: 004020 - Intelligence, Analysis & Operations

Position Summary

ANSER is seeking a Principal Computer Scientist to support AI/ML Quality Assurance Framework (QAF) efforts by integrating mathematics, statics, information science, AI, network science, probability modeling, data mining, data engineering, data warehousing, data compression, data protection, and/or other scientific techniques to correlate complex, technical findings into graphical, written, visual and verbal narrative products on trends of existing intelligence data to leverage other IC data sources.

Day-to-Day Responsibilities

• Review and evaluate QAF documentation submitted by Advanced Analytic (AA) owners to ensure compliance with tradecraft standards and adherence to best practices in AI system development and deployment.
• Assess QAF documentation for completeness, accuracy, and thoroughness, and provide detailed feedback to AA owners and developers.
• Develops and utilizes and data mining algorithms based on open-source capabilities. Integrates or codes algorithms to support Government intelligence search and discovery missions.
• Makes best practice recommendations on managing data within hardware, software, storage, and bandwidth constraints.
• Assist in maintaining a repository of QAF documentation to facilitate knowledge sharing and best practices across the organization.
• Provide consultation and guidance to AA owners, developers, and stakeholders on the QAF process, including best practices for Al system development, testing, and deployment.
• Collaborate with AA owners and developers to identify and address potential issues or gaps in QAF documentation.
• Develop and deliver training materials and workshops to educate AA owners, developers, and stakeholders on the QAF process and its application in AI system development.
• Assist analytic methodologists and AA owners in translating technical documentation into analytic tradecraft compliant language.
• Collaborate with stakeholders to develop, implement, and refine best practices for translating technical documentation into tradecraft compliant language.
• Review and edit translated documentation to ensure accuracy, completeness, and adherence to tradecraft standards.
• Supports capability development by contributing, editing, and storing code in Government owned/controlled source version control repositories.
• Employs data science techniques to support predictive analysis, social across the Defense Intelligence Enterprise (DIE).

Required Qualifications

• TS/SCI with Poly Clearance
• Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related technical field.
• A minimum of 20 years related experience in data science, analytics, or machine learning in support of intelligence or defense missions.
• High knowledge of TEVV methodologies, bias detection, and fairness analysis for AI systems.
• High Proficiency in Python, R, and SQL, and experience with data visualization tools such as Power BI or Tableau.
• High experience with predictive modeling, data mining, statistical analysis, and ML algorithms.
• Strong experience with knowledge graphs, semantic data modeling, and query languages (e.g., SPARQL, GraphQL).
• Strong experience developing, implementing, and evaluating AI/ML models for analytic efficiency and tradecraft compliance.
• Strong understanding of DoD 8570/8140 compliance standards for computer science and cybersecurity roles.

Preferred Qualifications

• Experience supporting DIA, IC, or DoD analytic modernization or OBI initiatives.
• Experience with distributed data processing, cloud-based analytics, or large-scale machine learning architectures.
• Demonstrated understanding of Object Based Intelligence frameworks.
• Experience with DoD 8570/8140 compliance and responsible AI policy frameworks.
• Experience working with in support of Defense Intelligence All-source Analytic Enterprise (DIAAE) OBI efforts, the Defense Intelligence Enterprise Knowledge Model (DIEKM), and Defense Intelligence Core Ontology (DICO).