Data Scientist - AI/LM (TS/SCI w/Poly)

Posted Yesterday
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Linthicum, MD, USA
In-Office
200K-220K Annually
Senior level
Other
The Role
Develop, deploy, and monitor machine learning and analytic capabilities for defensive cyber operations. Ingest and transform multi-source data, build data pipelines, validate operational models, collaborate with analysts to deliver actionable insights, and document processes for reproducibility in high-security environments.
Summary Generated by Built In
About Us:

Spry Methods is a proven provider of mission-focused technology, cybersecurity, and program management solutions supporting critical Federal and DoD missions. We specialize in delivering integrated, high-impact solutions across cyber operations, enterprise resource management, and mission support services. Our culture emphasizes collaboration, accountability, and innovation - empowering our teams to deliver meaningful outcomes in complex, high-security environments.  

This position is tied to a federal proposal effort. Employment is contingent upon contract award and successful completion of the customer's onboarding and security requirements. Start dates and final reporting instructions will be confirmed upon award. 

 
Who We’re Looking For (Position Overview):

Spry Methods is seeking an AI/LM Data Scientist to support Defensive Cyber Operations within a mission-focused environment. This role directly supports national security objectives by developing and deploying analytic capabilities that enable detection, analysis, and response to cyber threats. 

The Data Scientist will work within an integrated team of engineers, analysts, and mission stakeholders to transform complex, high-volume data into actionable insights. The position emphasizes applied analytics, model deployment, and operational impact—moving beyond research to deliver solutions that support real-time cyber operations. 

 

What Your Day-To-Day Looks Like (Position Responsibilities):

    Apply machine learning, statistical methods, and analytics to address cyber and intelligence-related challenges  

    Design, develop, and optimize models for pattern detection, classification, and anomaly identification  

    Ingest, transform, and integrate data from multiple sources to support analytic workflows  

    Build and maintain data pipelines and ensure data quality, consistency, and accessibility  

    Deploy analytic models into production and monitor performance for continuous improvement  

    Collaborate with analysts and operators to interpret results and refine analytic approaches  

    Translate technical findings into clear, actionable insights for stakeholders  

    Support rapid prototyping and delivery of new analytic capabilities in operational environments  

    Document data sources, models, and processes to ensure transparency and reproducibility  

    Stay current with emerging data science techniques and tools to enhance mission impact 

What You Need to Succeed (Minimum Requirements):

    • Active TS/SCI clearance with polygraph  
    • Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or related field  

    • 8+ years of experience supporting data science, analytics, or cyber-related programs (or equivalent combination of education and experience)  

    • 3+ years of experience supporting Defensive Cyber Operations environments  

    • 3+ years of hands-on experience with machine learning and statistical modeling  

    • Experience evaluating, validating, and implementing analytic models in operational environments  

    • Proficiency in one or more programming languages used for data analysis (e.g., Python, R)  

    • Experience using SQL or similar tools to manipulate and analyze data 

Ideally, You Also Have (Preferred Qualifications):

    Experience with:  

    • Cloud environments (e.g., AWS) 
    • Big data platforms and distributed data processing 

    • Cybersecurity frameworks, controls, or compliance requirements (e.g., STIGs, IAVAs) 

    • Strong technical background in:  

    • Linux (e.g., Ubuntu, CentOS, RHEL) and/or Windows environments 

    • Virtualized or hybrid infrastructure 

    • Experience with:  

    • Jupyter Notebooks and interactive data analysis tools 

    • Network analysis and cyber data sets 

    • Familiarity with:  

    • Automation and scripting for analytic workflows 

    • Software development practices and collaboration with engineering teams 

Skills Required

  • Active TS/SCI clearance with polygraph
  • Bachelor's degree in Computer Science, Data Science, Mathematics, Engineering, or related field
  • 8+ years supporting data science, analytics, or cyber-related programs (or equivalent)
  • 3+ years supporting Defensive Cyber Operations environments
  • 3+ years hands-on experience with machine learning and statistical modeling
  • Experience evaluating, validating, and implementing analytic models in operational environments
  • Proficiency in one or more data analysis programming languages (e.g., Python, R)
  • Experience using SQL or similar tools to manipulate and analyze data
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The Company
HQ: McLean, VA
89 Employees
Year Founded: 2000

What We Do

Spry is a certified Small Business headquartered in McLean, VA. Spry provides Enterprise, C4IT, Management, and Cyber Solutions to the federal government and commercial entities. Founded in 2001, Spry Methods was built on the foundation of combining industry knowledge with unmatched responsiveness to produce results for our customers. Our goal is to build a business dedicated to the maximization of value for all stakeholders starting with our employees, our customers, and our community. We recognize that talented and dedicated employees are our most valued assets and the foundation of our success. Guided by these principles, we have established an impressive track record of proven past performance serving our customers within the Commercial, Federal Civilian, DoD, and Intelligence Communities. A CMMI Level 3 certified and ISO 9001:2008 registered company, Spry is committed to quality and continuous improvement.

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