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VideoI bring a unique blend of advanced economics training, quantitative reasoning, and teaching experience that makes me strong in data labeling and AI training data work. Because of my background in econometrics, causal inference, behavioral and experimental economics, Bayesian methods, and statistical modeling, I’m skilled at spotting patterns, edge cases, and subtle logical inconsistencies—exactly the types of details that matter when producing high-quality training data. My experience teaching undergraduate and graduate students has trained me to evaluate clarity, coherence, and correctness in written reasoning, and I’ve created detailed grading rubrics and analytical tasks (including work for Outlier.ai) that translate directly into annotation, evaluation, and prompt-engineering workflows. I also bring multi-modal awareness from years in video production, scripting, and content development, which lets me label and assess text, image, and video data with attention to accuracy and narrative structure. I’m proficient in Python, R, Stata, SQL, and advanced statistical methods, so I understand how annotated data flows into model training and evaluation. Combined with my research experience, conference presentations, and broad teaching portfolio, I offer a data-labeling skillset that is analytical, detail-oriented, and domain-informed—well suited for high-accuracy AI training and reasoning-heavy annotation work.
Write high-complexity prompts in the domain of Economics and then wrote comprehensive rubrics.
Wrote prompts in my domain expertise of Economics and wrote Golden Responses.
PhD Candidate, Economics
Master of Arts, Economics
Part-time Instructor of Economics
Part-time Adjunct Professor of Economics
