Important
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.
Key Responsibilities
- Develop AI Training Content: Create detailed prompts and gold-standard answers across epidemiology, public health practice, policy, prevention, and risk communication topics.
- Optimize AI Performance: Evaluate and rank AI responses to improve correctness, clarity, contextual relevance, and scientific rigor.
- Ensure Model Integrity: Test AI outputs for inaccuracies, misleading evidence interpretation, unsafe recommendations, and bias; validate reliability across populations and settings.
- Bachelor’s degree (or higher) in Public Health (MPH preferred), Epidemiology, Biostatistics, Health Policy, Global Health, or a related field (or equivalent professional experience).
- 5+ years of professional experience in public health (government/public sector, NGOs, academia, healthcare systems, global health, consulting, or similar).
- Confident in public health reasoning (problem framing, causal thinking, bias/confounding awareness, risk stratification, and prioritization).
- Strong understanding of disease processes at the population level, prevention strategies, screening concepts, vaccination principles, and health behavior frameworks.
- Excellent attention to detail when fact-checking and identifying unsafe assumptions, misleading causal claims, incorrect interpretation of statistics, or inappropriate recommendations; minimum C1 English proficiency.
- Comfortable evaluating answers for internal consistency (denominators, rates vs risks, time horizons, surveillance definitions, and uncertainty).
- Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
- Prior experience with AI data training/annotation, guideline work, systematic reviews, program evaluation, policy analysis, or editorial QA is strongly preferred.