Remote PK/PD AI Modeling & Evaluation Scientist

DataAnnotation; Town of Florida (NY); Remote; USD 55,000 - 172,000; Full time

Benefits offered by this job

Remote work

Flexible projects

Job summary

DataAnnotation is seeking experienced computational biologists and bioinformaticians to help train AI models. You’ll design realistic analysis tasks from your own practice, run them through frontier AI systems, and grade what comes back against a professional standard.

You’ll write clear grading rubrics, explain why a response passes or fails, and flag concrete failures with evidence—batch effects, wrong tests, uncorrected p-values, or misused references—across genomics, transcriptomics, and

Qualifications

• 3+ years hands-on analyzing real biological data in industry, an academic lab, or a research institute.
• Write, debug, and explain your own analysis code in Python and/or R.
• Depth in at least one area: genomics, transcriptomics, proteomics, or population genetics.
• Owned a multistep analysis end-to-end from raw data to interpretation.
• Master’s or PhD (or current candidate) in biology, bioinformatics, biostatistics, CS, completed in the US/Canada/Europe/UK.
• Strong written English and comfort with ambiguity; familiarity with AI/LLM tools.

Responsibilities

• Design realistic analysis tasks from your workflows and required inputs.
• Run tasks through frontier AI agents and grade the deliverable against standards.
• Write clear grading rubrics and explain pass/fail criteria.
• Flag concrete failures with evidence such as batch effects or incorrect tests.

Skills

Python

R

Data analysis

Genomics

Education

Master's or PhD

Tools

Jupyter

RStudio

Claude

ChatGPT

Job description

DataAnnotation is seeking experienced computational biologists and bioinformaticians to help train AI models. You’ll design realistic analysis tasks from your own practice, run them through frontier AI systems, and grade what comes back against a professional standard.

You’ll write clear grading rubrics, explain why a response passes or fails, and flag concrete failures with evidence—batch effects, wrong tests, uncorrected p-values, or misused references—across genomics, transcriptomics, and