Principal Data Scientist - On-Site, Mission-Critical AI/Analytics

SAIC; Chantilly (VA); On-site; USD 140,000 - 180,000; Full time

Job summary

SAIC is hiring a Data Scientist Principal to support a SETA program for the National Reconnaissance Office in Chantilly, VA. You will analyze complex data to inform mission decisions, guide analytic strategy, and advise leadership on model risk and data-informed tradeoffs.

Responsibilities include designing data sets, integrating large data, ensuring data quality, collaborating with data scientists and engineers, documenting processes, and supporting testing and compliance with regulations.

Qualifications

• Bachelor-level plus 9+ years of experience; Master’s 7+; PhD/JD 4+ (degree substituted by experience).
• Active Top Secret/SCI with Poly.
• 5+ years of software engineering with IC, DoD, or commercial space architectures.
• Experience in data analytics, ML, statistics.
• Experience applying AI/ML techniques.
• Demonstrated initiative, problem-solving, and teamwork.
• Strong oral and written communication in technical, high-tempo environments.
• MS Office proficiency.

Responsibilities

• Design and manage data sets for interoperability.
• Integrate large, complex data sets for cross-organizational use.
• Evaluate data quality and efficiency across data lifecycle.
• Collaborate with data scientists, developers, operators, PMs, and clients.
• Document process improvements to automate data delivery.
• Support testing and validation of operational platforms.
• Develop technical documentation on processes and system specs.
• Ensure data compliance and promote ethical use of platforms.

Skills

Data analytics

Machine learning

Statistics

Communication skills

Team collaboration

MS Office

Education

Bachelor's degree

Master's degree

PhD/JD

Job description

SAIC is hiring a Data Scientist Principal to support a SETA program for the National Reconnaissance Office in Chantilly, VA. You will analyze complex data to inform mission decisions, guide analytic strategy, and advise leadership on model risk and data-informed tradeoffs.

Responsibilities include designing data sets, integrating large data, ensuring data quality, collaborating with data scientists and engineers, documenting processes, and supporting testing and compliance with regulations.