Role Title: Data Science & Analytic Methodology SME

Role Type: Contract

Location: Remote

Required Skills:

• LLMs
• Pipeline integration
• Data analytics
• Scaled-analysis judgment
• Data science

In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required - your domain knowledge is what matters.

Scope of Work:

• Map scaled-analysis and pipeline-integration scenarios to an established taxonomy across key evaluation areas.
• Author detailed analytic cases that reflect authentic deployment patterns, including automated triage, enrichment, and analysis-at-scale, as well as representative failure modes in real-world settings.
• Evaluate and grade responses based on criteria such as source handling, reasoning, understanding of limitations, and practical utility.
• Identify and flag instances of model over-refusal that could hinder legitimate analytic workflows or disrupt pipeline continuity.
• Document challenging edge cases, emerging methodology concerns, and provide actionable recommendations for continuous improvement.
• Contribute clear, insightful written and verbal feedback that supports iterative, high-quality project outcomes.

Preferred Qualifications:

• Demonstrated experience integrating LLMs—such as government GPT variants—into complex data or analytic pipelines.
• Proven capability in conducting analysis at scale, with an understanding of automation failure modes and relevant evaluation metrics.
• Track record of teaching, mentoring, or technical leadership in data science or related analytic disciplines.
• Expertise in authoring clear, concise technical documentation and conveying complex concepts through strong written English.
• Exposure to the design of evaluation frameworks or benchmark systems.
• Familiarity with defense, intelligence, or government analytic domains is highly valued.
• Ability to operate effectively in distributed, international project environments with minimal clearance constraints.