Mwenje F.

Key Skills

Software

Don't disclose

Top Subject Matter

Scientific abstract evaluation

misinformation/bias detection

multilingual data quality assurance

Top Data Types

Text

Document

Top Task Types

No task types listed

Freelancer Overview

International Abstract Reviewer (World Federation of Public Health Associations (WFPHA)). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Doctor of Philosophy, Sorbonne University (Université Paris-Sorbonne) and Master of Arts, Masinde Muliro University of Science and Technology. AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

International Abstract Reviewer (World Federation of Public Health Associations (WFPHA))

Don't discloseText

Performed multilingual scientific abstract verification as a primary quality gatekeeper for peer-review submissions, validating factual correctness, structural logic, and data accuracy. Applied multi-tier evaluation frameworks to score complex abstract datasets under strict guidelines and detect logical gaps or source discrepancies. Ensured bias and misinformation risks were identified across English and French inputs by using rigorous analytical review criteria. • Validated factual correctness and data accuracy for global submissions • Scored abstracts using structured, evolving review frameworks • Detected non-obvious misinformation and source discrepancies • Reviewed multilingual inputs to improve analytical integrity and trust

2026 - Present

Freelance Language Operations Specialist & French LQA Auditor (TransPerfect / Welocalize / e2f Inc. partner networks)

Don't discloseText

Audited AI-generated French outputs against strict ground-truth parameters, assessing linguistic naturalness, contextual utility, and factual exactness. Executed fact-checking workflows by cross-referencing claims against verified databases to isolate hallucinations, bias, and cultural misalignment. Provided structured, guideline-driven feedback to support iterative improvement of localized datasets and LLM-related quality. • Performed French language quality assurance (LQA) and linguistic auditing • Conducted hallucination detection via claim verification against sources • Produced structured error logs and edge-case justifications for ML teams • Adapted to iterative annotation/LQA guidelines while maintaining quality benchmarks

2023 - Present

Education

Work History