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AfterQuery is seeking statisticians and experimentation experts to author and grade causal inference and A/B testing scenarios. This remote, fully async contractor role pays $80–$150 per hour and requires 10+ hours weekly.
You’ll design studies, write solution rationales, and evaluate model outputs to ensure methodological soundness. Ideal candidates hold an advanced degree in statistics or biostatistics (PhD preferred) and have 2+ years of applied statistics or experimentation experience, with
Statisticians and experimentation experts in causal inference and A/B testing to build the scenarios frontier AI labs use to test model reasoning. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models. We're hiring statisticians and experimentation experts as contractors to author and grade the causal inference and A/B testing scenarios those evaluations run on. What you'll actually do Author realistic statistics scenarios, from study design through analysis and interpretationWrite reference answers with the statistical reasoning behind each modeling choiceGrade model output for methodological soundness and correct interpretationFlag analyses that read rigorous but draw invalid conclusions Who this is for Statisticians and biostatisticians with an advanced degree in a quantitative fieldPractitioners with at least 2 years of applied statistics or experimentation workExperts with depth in experimental design, causal inference, or A/B testingAnalysts fluent in R or Python who can document the reasoning behind a modeling choice Contract and pay $80 to $150/hr, paid weekly via Stripe. Your rate is set from your experience, not negotiated down after you start.Remote, fully async. No fixed hours.Minimum 10 hours per week, no maximum. Scale up or down week to week.No screen recording, no keystroke monitoring, no idle timers.Independent contractor. Ongoing project work rather than single task batches. About AfterQuery AfterQuery is an expert data lab. We build the evaluation and training data that frontier AI labs use to measure what their models can and can't do in real professional work. Our contributors are practicing professionals, not annotators, and the work is credited, reviewed, and paid at professional rates.