Statistician & Experimentation Expert AI Evaluation

OpenTrain AI, Inc.; United States; Remote; USD 83,000 - 152,000; Full time

Job summary

OpenTrain AI, Inc. seeks a remote, ongoing contractor to craft realistic statistics scenarios and evaluate AI outputs for causal inference, experimental design, and A/B testing.

You will use expert statistical judgment to assess model outputs, explain reasoning, and flag invalid conclusions. The role emphasizes methodological rigor, ability to communicate decisions clearly, and experience with Bayesian approaches, instrumental variables, or synthetic controls.

Qualifications

• Advanced training in statistics with applied experience.
• Ability to explain technical decisions clearly.
• Experience with large-scale experimentation platforms.
• Fluent in R or Python for statistical analysis.

Responsibilities

• Write realistic statistics scenarios about study design, analysis, and interpretation.
• Create reference answers that explain the reasoning behind modeling choices.
• Evaluate AI outputs for methodological soundness and accurate interpretation.
• Identify analyses that use an apparently rigorous approach but draw invalid conclusions.
• Apply expertise in experimental design, causal inference, and A/B testing.

Skills

Statistical analysis

Experimental design

Causal inference

A/B testing

R

Python

Education

Advanced degree in statistics/biostatistics or related field

PhD in statistics/biostatistics/econometrics (relevant background)

Tools

Bayesian methods

Instrumental variables

Synthetic control methods

Large-scale experimentation platforms

Job description

Create realistic statistics scenarios and evaluate AI outputs for causal inference, experimental design, and A/B testing. This ongoing remote contractor role offers flexible scheduling with at least 10 hours per week.

Remote Hourly pay

Hourly

Compensation

Contract

Engagement

Remote

Location

Posted

Open in 95 countries

• Albania
• Algeria
• Antigua and Barbuda
• Armenia
• Australia
• Austria
• Bahamas
• Bahrain
• Belgium
• Benin
• Bhutan
• Bosnia and Herzegovina
• Botswana
• Brunei
• Bulgaria
• Canada
• Costa Rica
• Côte d’Ivoire
• Croatia
• Cyprus
• Czechia
• Denmark
• Ecuador
• El Salvador
• Estonia
• Ethiopia
• Finland
• France
• Gambia
• Germany
• Greece
• Guatemala
• Guyana
• Hong Kong
• Hungary
• Iceland
• India
• Indonesia
• Ireland
• Israel
• Italy
• Jamaica
• Jordan
• Kenya
• Kuwait
• Latvia
• Liechtenstein
• Lithuania
• Luxembourg
• Madagascar
• Malaysia
• Malta
• Mauritius
• Mexico
• Moldova
• Mongolia
• Morocco
• Mozambique
• Namibia
• Netherlands
• New Zealand
• North Macedonia
• Norway
• Oman
• Pakistan
• Panama
• Peru
• Philippines
• Poland
• Portugal
• Qatar
• Romania
• Rwanda
• Saint Lucia
• Senegal
• Serbia
• Singapore
• Slovakia
• Slovenia
• South Africa
• Spain
• Sri Lanka
• Sweden
• Switzerland
• Taiwan
• Tanzania
• Thailand
• Trinidad and Tobago
• Tunisia
• Türkiye
• United Arab Emirates
• United Kingdom
• United States
• Uzbekistan
• Vietnam

The work

You will create and evaluate AI training data focused on causal inference and A/B testing. The work tests whether AI models can reason correctly about study design, statistical analysis, and results.

You will use expert statistical judgment to assess model outputs, explain sound reasoning, and identify conclusions that appear rigorous but are not valid.

• Write realistic statistics scenarios about study design, analysis, and interpretation.
• Create reference answers that explain the reasoning behind modeling choices.
• Evaluate AI outputs for methodological soundness and accurate interpretation.
• Identify analyses that use an apparently rigorous approach but draw invalid conclusions.
• Apply expertise in experimental design, causal inference, and A/B testing.

What it pays and takes

This is an ongoing, fully remote independent contractor role. The schedule is asynchronous, with flexibility to adjust your workload from week to week.

The role requires advanced statistical training and applied experience, along with the ability to explain technical decisions clearly.

• Work at least 10 hours per week, with no maximum.
• Open to candidates in the countries listed for this role.
• Hold an advanced degree in statistics, biostatistics, or a closely related quantitative field.
• Have at least two years of applied statistics or experimentation experience.
• Have strong knowledge of experimental design, causal inference, or A/B testing.
• Be fluent in R or Python for statistical analysis.
• Document and explain modeling decisions clearly.
• Experience with large-scale experimentation platforms, Bayesian methods, instrumental variables, or synthetic control methods is helpful.
• A PhD in statistics, biostatistics, or econometrics is relevant background.
• Payment is made weekly via Stripe.