Biostatistician

What you would do

• Review AI-produced tables, figures, and listings for mathematical and statistical accuracy
• Verify analyses against statistical analysis plan specifications
• Identify errors in populations, censoring, missing data, and multiplicity handling
• Assess narrative clinical study report claims against supporting statistics
• Document ground truth and reasoning for each evaluation finding

Who they want

• 5+ years as biostatistician at sponsor, CRO, or academic trials unit
• Hands-on experience producing and QC-ing TFLs for regulatory submissions
• Proficiency in SAS and/or R for independent analysis reproduction
• Understanding of confirmatory trial methods (survival, mixed models, covariate adjustment)
• Advanced degree in Biostatistics, Statistics, or related quantitative field

Main skills

What the interview asks about

1. 1.Detecting population definition errors

   Subtle changes in inclusion/exclusion criteria create different denominators; biostatisticians must catch inconsistencies.

   For example: “The SAP specifies 'patients with baseline lab value greater than 100 and safety follow-up data'; an AI-generated table excludes 5 patients without baseline labs but includes 3 patients missing final safety data. How do you approach verifying which is correct?”

2. 2.Assessing multiplicity control

   Many statistical tests inflate false positive rates; biostatisticians must verify that multiplicity adjustments match the SAP.

   For example: “The SAP pre-specifies 4 efficacy endpoints with Bonferroni correction (alpha=0.0125). You see an AI report with 4 p-values; one is 0.012, reported as significant. Do you flag this as an error?”

3. 3.Validating survival analysis censoring

   Incorrect censoring logic produces wrong Kaplan-Meier curves and hazard ratios; biostatisticians must verify the derivation.

   For example: “A survival analysis shows median overall survival as 180 days based on 10 events in 150 patients. You pull the dataset and verify 2 additional patients reached the event after the analysis cutoff. Does this indicate a data or logic error?”

4. 4.Missing data handling consistency

   Missing data strategies must be pre-specified and applied uniformly; AI may miss exceptions or apply inconsistent logic.

   For example: “The SAP specifies LOCF (last observation carried forward) for missing efficacy data, but also ICH E9(R1) missing-at-random sensitivity analyses. An AI report shows only LOCF results. What additional validation would you request?”

5. 5.Distinguishing valid alternatives from errors

   Multiple statistical approaches can be valid; biostatisticians must separate defensible choices from true mistakes.

   For example: “The SAP allows either Dunnett or Bonferroni for multiple comparison correction. An AI report uses Sidak instead. Is this a deviation that needs flagging, or a reasonable alternative you would accept?”

A task you may get

Evaluate 2 AI-generated clinical analysis summaries (each 3-5 tables with narrative), verify against a mock SAP and dataset specification, and identify all errors vs. acceptable alternatives.

How to prepare

• Review recent clinical trials you worked on, noting how statisticians catch errors in TFLs and CSRs.
• Study common biostatistical mistakes: population definition shifts, censoring logic errors, multiplicity errors, missing-data assumption violations.
• Refresh your knowledge of ICH E9(R1) guidance on estimands and missing-data handling.
• Prepare an example where you discovered an error in a regulatory submission and how you corrected it.

The facts

Pay

$60–65/hr

Open to

Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia

Field

Medicine

Role type

Specialist

Posted

8/4/2026

Places left

30

We wrote this page from the public micro1 listing. It may be out of date, so read the full posting before you apply.