Data Scientist Expert
Listing checked October 8, 2026 · pay as published by AfterQuery
Overview
AfterQuery assembles the expert evaluation data that frontier AI labs rely on to test and sharpen their models. This contract role puts practicing data scientists to work authoring the analysis problems those evaluations run on, pulled straight from your own daily work in experiments, metrics, forecasting and predictive modeling. You write each problem, solve it end to end with a reference answer, and flag the mistakes that pass a quick look but fail under a practitioner's eye. Work is fully remote and async: no fixed hours, and you decide how much or how little you take on each week.
What You'll Do7
- 1Draft realistic data science problems grounded in your own project history, covering experimentation, metric design, forecasting, segmentation and predictive modeling.
- 2Solve every problem you write, producing a reference answer with the reasoning shown at each step.
- 3Spot the analytical errors that read as sound to a generalist but break under scrutiny from someone who does the work.
- 4Document the method behind each solution so a reviewer can trace how you reached the result.
- 5Match the difficulty and framing of problems to what evaluation sets need.
- 6Review and refine your submissions when feedback comes back from the review pipeline.
- 7Keep the analytical bar high across every problem you contribute.
Requirements8
- 1Master's or PhD in statistics, computer science, economics, mathematics or a related quantitative field.
- 2At least 3 years of full time work as a data scientist. Internships and academic projects do not count toward this.
- 3Strong SQL plus Python or R.
- 4Hands-on statistics work: A/B testing, regression, causal inference or predictive modeling.
- 5Availability of 10 hours per week or more.
- 6Experience on a product, growth or marketplace team (preferred).
- 7Track record of designing experiments and presenting the readout (preferred).
- 8Clear written English and the ability to walk through an analysis step by step (preferred).
Who Should Apply
The right fit is a working data scientist with a graduate degree in a quantitative field who spends their days on modeling, statistics and experiment design rather than dashboards. Product, growth and marketplace backgrounds help, and you need at least 3 years of full time data science work plus 10 hours a week to give. Applicants who only build pipelines or reporting layers will not match this role, and neither will data engineers or ML engineers. A common reason candidates get filtered out is counting internships or coursework toward the 3 year requirement. Another is thin written reasoning: this work lives or dies on explaining an analysis step by step, so vague writeups score low even when the answer is right.
Salary Insight
The listing pays $115.00 per hour, a rate that sits well above typical data science contract work and reflects the seniority and graduate-level statistics the role demands. Expect to set your own weekly hours within that rate, and scale your commitment up or down as your schedule allows. Payment arrives weekly through Stripe.
Pay and demand for Data Engineering roles
AggregatedTypical pay
$75/hour
This role
$115/hr
Most Data Engineering roles pay $50–$100 per hour. This role's pay sits above that range.
Based on 649 similar roles that publish pay · 180 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 767
- Listed in last 30 days
- 518
- Remote
- 96%
Hiring most right now: micro1 (255) · SME Careers (147) · Mercor (94)
Most requested skills · share of roles
- python17%
- ai training15%
- llm evaluation15%
- trainer feedback10%
Figures from Data Engineering roles live on NearSkill when this page loaded. A role can close before you apply, so check the listing itself.
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Compensation
$115/hr
Required Skills
Application Tip
Lead with the quantitative credentials and the years of full time data science work, since the listing screens out internships and school projects. Then add one short example of an experiment or model you designed, including the readout, to show you can explain an analysis step by step in writing. Naming exact methods such as A/B testing, causal inference or forecasting will help your fit score more than a general claim of statistics experience.
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