The work
You will create data science problems grounded in real practice, then solve each one with a defensible reference answer and explicit reasoning. Topics may include experiment design, metric definition, forecasting, segmentation, regression, causal inference and predictive modeling. You will also identify answers that appear plausible but contain methodological errors, leakage, unsupported assumptions or incorrect interpretations.
What AfterQuery screens for
The screening focuses on whether you have at least three years of full-time data science experience and can discuss specific work under detailed follow-up. Expect to explain analytical choices, assumptions, validation methods and failure modes rather than recite definitions. A master's or PhD in statistics, computer science, economics, mathematics or another quantitative field is required. Strong SQL and Python or R are required.
Relevant background
This role is intended for practicing data scientists with hands-on modeling and statistics experience, not data engineers, ML engineers or reporting-only analysts. Experience on a product, growth or marketplace team is preferred, as is experience designing and reading out experiments. Clear written English is preferred because each reference answer must explain the analysis step by step.
Logistics
The contract is fully remote and async, with no fixed hours. You must be able to commit at least 10 hours per week, while setting your own schedule and scaling your workload up or down from week to week. Payment is weekly via Stripe.
Pay band: $115/hr