Data Science & Analytic Methodology SME, Remote- Contract
First seen by Alion on Oct 27, 2026.
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.
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.
Originally posted on Himalayas