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 interpretation Write reference answers with the statistical reasoning behind each modeling choice Grade model output for methodological soundness and correct interpretation Flag analyses that read rigorous but draw invalid conclusions Who this is for Statisticians and biostatisticians with an advanced degree in a quantitative field Practitioners with at least 2 years of applied statistics or experimentation work Experts with depth in experimental design, causal inference, or A/B testing Analysts 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. How our hiring process works Apply at /apply/statistician-experimentation-expert. About five minutes. Resume plus a few structured questions. Resume review. Every application gets read against a published rubric and gets a decision. Written assessment. You may be asked to take a written assessment for further placement. Onboarding. Sign the contractor agreement, connect Stripe, and get platform access. Project match. You're matched to live work in your specialty, usually within a week of onboarding. 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.
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