Remote Contract Statistician: Causal Inference & A/B Tests

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

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.