Data Scientists: $70-$180/hr from Frontier AI Labs

Open application· 96 spots this round

$70-$180/hr data science and machine learning work, on your schedule

Review AI-generated notebooks, model evaluations, and statistical analysis the way you'd review a teammate's pull request. Catch the data leakage, the p-hacking, the silently wrong baseline. Your instinct for what makes a result trustworthy - the kind earned shipping models that survived production - is exactly what AI labs are short on.

Fully remoteYour scheduleWeekly pay

Apply nowApply once, get matched on a rolling basis. No prior AI experience needed.

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Hi, we're Zac and Jack, the founders of Terac. We want to talk to you directly, because you are the most important part of what we're building.

Terac is a community of experts. People who have spent years getting good at something specific and hard. The world is about to need more of you, not less. As AI takes on more of the world's work, the bottleneck shifts to the people who actually know what they're talking about.

Expert labor is the rarest resource in the world right now, and it is shockingly hard to find. The companies that need a data scientist's eye on a leaky validation set spend weeks chasing people, paying placement fees, and settling for whoever is available. Meanwhile thousands of qualified people are sitting with knowledge that no one ever asks for.

That gap is what we're here to close. Every project that lands on Terac is routed to the people who actually know the answer, on their schedule, paid fairly, and only when the work is verified. No middleman taking a cut of your time. No vague gigs. No chasing checks.

We care about every single person in this community. If you join Terac, you're not a row in a database to us. We read the feedback. We answer the emails. We will fight for you when a customer is being unreasonable, and we will be honest with you when something on our side is broken. The quality of this panel is our entire company, and we owe you a serious bar.

If you've made it this far, here is what we're asking: claim your profile. Put your expertise on the record. Let the world's most ambitious teams come find you for the work only you can do.

Zac & Jack

Founders

Data Science questions

Still curious? Write to us at support@terac.com.

Narrow sub-specialties are among the most in-demand profiles we have. Models trained on generic data science content consistently fail at field-specific reasoning, so experts who can evaluate causal identification strategies, embedding fine-tuning approaches, or time-series forecasting pipelines are more valuable than generalists. If you have deep experience in any one sub-domain, that focus is an asset, not a limitation.

The work varies by project, but typical deliverables include AI-generated Python or R analyses, model evaluation write-ups, feature engineering rationale, and statistical methodology explanations. You may also be asked to produce worked examples showing how you would approach a specific problem, such as selecting a regularization strategy or diagnosing data leakage, so that the model can learn from your reasoning process.

Credentials like the CAP or an advanced degree in statistics, applied math, or a quantitative social science do help us route you to projects that require verified domain authority, particularly those involving regulated industries like finance or healthcare where the AI outputs must meet a higher standard of methodological rigor. They are not required to participate, but listing them in your profile ensures you are considered for those higher-complexity assignments.

No. All data used in projects is either fully synthetic, publicly licensed, or anonymized before it reaches you, and no task requires you to process, store, or transmit real personal data. Project briefs specify the data context upfront, so if a scenario involves healthcare or financial records it will be clearly simulated, and you are never put in a position where your participation would conflict with data protection regulations you are professionally obligated to follow.

Yes, and that background is genuinely underrepresented. Much of the AI training content for data science skews toward academic or Kaggle-style workflows, so reviewers with production experience using SQL-heavy pipelines, distributed compute, or modern data stack tooling provide coverage that is otherwise hard to source. Projects that involve data engineering adjacent reasoning, scalability tradeoffs, or enterprise ML deployment are a strong match for that background.

Why your expertise matters

Data scientists develop the judgment to know when a model's output is statistically plausible but analytically wrong - catching subtle issues like data leakage, inappropriate feature engineering choices, or a misapplied hypothesis test that a non-specialist would accept at face value. Frontier models are increasingly asked to write Python and R analysis pipelines, interpret regression diagnostics, and recommend ML architectures, and only a working practitioner can reliably judge whether those outputs reflect real analytical rigor or confident-sounding hallucination. The field's breadth across statistics, engineering, and domain context means that shallow AI outputs are especially hard to detect without someone who has shipped production models and defended methodology under scrutiny.

How pay works

Pay within the $70-$180/hr band reflects the depth and specificity of your sub-specialty: reviewers with production experience in areas like causal inference, time-series forecasting, or ML model risk tend to land toward the upper end, as do those who can evaluate both the statistical validity and the software engineering quality of an analysis. All work is fully remote, billed by the verified hour or by the completed task depending on assignment type, and payment is released only after the platform confirms your submission meets the defined quality bar - there are no retainers or minimums.

What the work looks like

A sample of the data science and machine learning work you would pick up. Every project is scoped, remote, and paid on verified completion.

Review an AI-generated Python notebook that performs customer churn prediction and flag any data leakage between the train and test splits, inappropriate imputation choices, or metric selection errors that would mislead a product team.
Evaluate an AI-written explanation of p-value interpretation and confidence intervals intended for a non-technical business audience, correcting any statements that overstate certainty or conflate statistical significance with practical significance.
Write a worked example of a proper difference-in-differences analysis on a synthetic dataset, annotating each step with the assumptions being invoked and the diagnostic checks a reviewer should expect to see.
Assess an AI-generated feature importance report from a gradient boosting model and identify whether the commentary correctly distinguishes permutation importance from SHAP values and whether the conclusions are supported by the numbers shown.
Stress-test an AI-produced time-series forecast by constructing adversarial input scenarios - such as regime changes or sparse seasonal data - and document where the model's recommendations break down.
Judge whether an AI-authored SQL query implementing a cohort retention analysis correctly handles edge cases like users with multiple first-event timestamps or sessions that span UTC midnight.