Math Expert (SME) - Freelance Project

<h3><strong>Purpose</strong></h3> <p>We are seeking a highly experienced Pure Mathematics professional to serve as a consultant on AI training data projects for leading AI model builders and research enterprises. Your focus will be to define success criteria, review outputs, and provide precise mathematical guidance to improve quality and consistency — directly contributing to the successful delivery of domain-specific annotated datasets, problem sets, and mathematical proofs that meet rigorous technical and academic standards. You will be engaged on specific projects with defined deliverables, milestones, and completion dates.</p> <h3><strong>Components</strong></h3> <h4><strong>Technical Standard Setting, Quality Control, and Process Improvement</strong></h4> <ul> <li>Define domain-specific quality success metrics for pure mathematics datasets, including rigor, logical soundness, and notational consistency.<br><br></li> <li>Develop project-specific SOPs, QA rubrics, and reference materials to ensure all work meets the highest mathematical standards.<br><br></li> <li>Review project outputs (proof annotations, theorem classifications, symbolic reasoning samples) for correctness, coherence, and fidelity to formal logic.<br><br></li> <li>Conduct structured QA passes on daily or weekly deliverables; track and resolve defects efficiently to maintain delivery timelines.<br><br></li> <li>Return work to contributors with clear remediation notes identifying logical missteps, notation errors, or stylistic inconsistencies.<br><br></li> <li>Provide expert input on tools, notation formats, and workflows (e.g., LaTeX conventions, symbolic logic frameworks, or theorem databases) to ensure precision and reproducibility.<br><br></li> <li>Handle spec changes and edge cases, such as nonstandard proof techniques, rare theorem dependencies, or novel symbolic representations, drafting updated acceptance criteria or reference examples.<br><br></li> <li>Curate “gold standard” libraries of proof samples, theorem statements, and annotated datasets for calibration and comparability across projects.</li> </ul> <h4><strong>Talent Vetting Output Improvement</strong></h4> <ul> <li>Participate in the technical vetting of mathematical contractors and contributors, including reviewing proof-based assessments and reasoning tasks.<br><br></li> <li>Evaluate sample outputs for logical rigor, clarity, and adherence to formal mathematical structure.<br><br></li> <li>Provide structured, actionable feedback to improve the precision and depth of mathematical reasoning.<br><br></li> <li>Develop training and calibration resources, such as:<br><br></li> <ul> <li>Guidelines for proof construction and logical argumentation<br><br></li> <li>Standards for symbolic notation and LaTeX formatting<br><br></li> <li>Reference sheets for key areas (e.g., abstract algebra, topology, number theory, set theory, mathematical logic)<br><br></li> <li>Examples of formal vs. informal proof presentation for alignment on project tone and purpose</li> </ul> </ul> <h4><strong>Project Delivery Support</strong></h4> <ul> <li>Advise on technical scoping and requirements during project setup, including domain selection (e.g., algebra, topology, analysis), dataset formats, and proof annotation frameworks.<br><br></li> <li>Provide expert guidance on edge cases and exceptions, such as nonconstructive proofs, category-theoretic formulations, or advanced symbolic expressions.<br><br></li> <li>Contribute to post-project reviews to capture lessons learned and strengthen future quality standards.<br><br></li> <li>Identify and summarize recurring error patterns, such as incorrect inference chains, misuse of quantifiers, or notation drift.<br><br></li> <li>Build defect tracking systems to monitor issue categories and trends, highlighting areas needing process or training adjustments.<br><br></li> <li>Conduct post-mortems to analyze error trends, clarify ambiguous specifications, and recommend workflow or ru ... (truncated, view full listing at source)