Clinical Trial Biostatistics AI Evaluator

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We connect qualified contributors with projects where human expertise helps improve how artificial intelligence understands, evaluates, and produces information.

Creating an OpenTrain account is free, and contributors can build a professional profile around specialized experience while discovering flexible remote opportunities.

About AI Training and Evaluation

AI training is the human side of building modern artificial intelligence. Subject-matter experts review examples, assess model outputs, write feedback, and establish reliable ground truth so AI systems can produce more accurate and useful results.

In this role, your clinical-trial statistics expertise will help evaluate whether AI-assisted research work is statistically correct, reproducible, and consistent with prespecified methodology.

The Clinical Trial Biostatistics AI Evaluator Role

OpenTrain is seeking a contract Clinical Trial Biostatistics AI Evaluator to review AI-assisted clinical research work. You will combine rigorous biostatistical judgment with the creation and review of evaluation examples that help AI systems reason about clinical trials.

This remote, part-time contractor opportunity requires a minimum commitment of 20 hours per week. The advertised rate is $60–$65 per hour.

• Employment type: Contract, part time
• Work arrangement: Remote and worldwide
• Time commitment: 20+ hours per week
• Compensation: $60–$65 per hour
• Primary language: English

What You'll Do

You will author and review evaluation tasks that require the derivation, reproduction, or validation of statistical outputs from clinical datasets and related tables, figures, and listings. You will establish defensible ground truth and document derivations clearly enough for another reviewer to verify independently.

You will also write concise rationales, distinguish statistical errors from acceptable methodological alternatives, and collaborate with a multidisciplinary team to refine evaluation tasks, criteria, and statistical feedback.

• Assess estimates, confidence intervals, and p-values against the applicable statistical analysis plan.
• Check analysis populations, censoring rules, multiplicity handling, and missing-data strategies.
• Compare statistical outputs with narrative descriptions in clinical study reports.
• Identify discrepancies in population definitions or statistical methodology.
• Reproduce analyses from written specifications using SAS and/or R.
• Create evaluation examples that support reliable AI assessment of clinical research.

Requirements

This role requires at least five years of biostatistics experience supporting clinical trials at a sponsor, contract research organization, or academic trials unit. You should be able to interpret statistical analysis plans and verify that reported results follow prespecified analyses.

An MSc or PhD in Biostatistics, Statistics, or a closely related quantitative field is sought. Strong knowledge of clinical-trial statistical methods and hands-on experience with regulatory datasets are essential.

• Five or more years supporting clinical trials as a biostatistician.
• Hands-on production or quality control of regulatory tables, figures, and listings.
• Direct experience working with CDISC SDTM and ADaM datasets.
• Proficiency in SAS and/or R, with the ability to reproduce analyses from specifications.
• Expert judgment on estimates, confidence intervals, p-values, populations, censoring, multiplicity, and missing data.
• Ability to evaluate methodology against a statistical analysis plan.
• MSc or PhD in Biostatistics, Statistics, or a closely related quantitative field.

Helpful Background

The following experience is helpful for evaluating a broader range of clinical research tasks and statistical outputs.

• Serving as lead statistician on pivotal or registrational studies.
• Authoring or reviewing statistical sections of clinical study reports.
• Experience with oncology endpoints.
• Use of AI-assisted statistical review tools.
• Knowledge of survival analysis, mixed models, covariate adjustment, and estimands.
• Familiarity with multiplicity control and missing-data strategies under ICH E9(R1).