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Loading LAYIQ…Job opportunity
Chantilly, VA
Source: Agile Defense careers · View original posting
From Agile Defense's posting. “We” and “our” refer to the employer.
At Agile Defense we know that action defines the outcome and new challenges require new solutions. That’s why we always look to the future and embrace change with an unmovable spirit and the courage to build for what comes next.
Our vision is to bring adaptive innovation to support our nation's most important missions through the seamless integration of advanced technologies, elite minds, and unparalleled agility—leveraging a foundation of speed, flexibility, and ingenuity to strengthen and protect our nation’s vital interests.
1355
Senior Data Scientist/Engineer
Chantilly, VA
Active DoD - TS/SCI FSP
Non-Employee Referral Bonus Eligible: $2,000
SUMMARY
You will join a secure, on-site intelligence data-analysis initiative focused on empowering end users to rapidly sift through vast cyber datasets and surface actionable insights. Working alongside cyber subject-matter experts, data engineers, and analysts, the program delivers a next-generation analytics tool that combines natural language processing, entity extraction, and generative AI to accelerate decision making in a highly classified environment.
As a Senior Data Scientist, you will design, develop, and deploy advanced data-science models and workflows within a cross-functional team. You will translate complex requirements into scalable algorithms, collaborate on data-ingestion and parsing tasks, and iteratively refine prototypes to meet end-user needs. Your work will directly inform the core analytics capabilities of the program’s user interface.
JOB DUTIES AND RESPONSIBILITIES
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Lead the end-to-end development of NLP and entity-extraction models tailored for cyber data.
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Prototype and integrate generative AI solutions to automate insight generation.
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Assist with data-engineering tasks, including parsing raw logs and preparing feature sets.
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Collaborate with cyber SMEs to define data-quality standards and validation criteria.
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Translate user stories and requirements into technical specifications and test plans.
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Implement, document, and maintain Python code for reproducible data-science workflows.
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Conduct model performance evaluations and iterate to improve accuracy and scalability.
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Present findings and demos to stakeholders, incorporating feedback into subsequent sprints.
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