Remote Climate Data Scientist - AI Trainer ($50-$60 per hour)Mercor • Lafayette, Louisiana, US
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Remote Climate Data Scientist - AI Trainer ($50-$60 per hour)
Mercor • Lafayette, Louisiana, US
[job_card.variable_days_ago]
[job_preview.job_type]
- [job_card.full_time]
- [filters.remote]
[job_card.job_description]Ideal Qualifications : Have a PhD in Climate Science, Earth Science, Environmental Science, Geophysics, Atmospheric Science , or related field from a top US / Canada / UK / New Zealand / Australia university. - Have experience with environmental databases (NOAA, EPA, EIA, USGS, NASA Earth Data). - Have 2+ years of professional research experience in climate science, environmental monitoring, or geophysical data analysis. - Have high attention to detail. Have exceptional written and verbal communication skills. Have excellent proficiency in English. - Are currently based in the U.S., Canada, New Zealand, UK, or Australia. Key Responsibilities : You will work asynchronously with a team of highly qualified experts across your domain. - You will craft climate analysis tasks involving environmental database searches, climate data extraction, and geophysical data retrieval across government portals. - You will create detailed evaluation rubrics for AI agent performance. - The project is 15-20 hours of work per week with potential for increased hours and extension. Role Start Date This role will begin in early December with applications reviewed on a rolling basis. Interview Process You will complete a short interview to assess your domain expertise. - You will be paid for up to 1 hour of onboarding time including the screening process and a few onboarding videos if you are hired.
Mercor is seeking
- Climate Data Scientists
- for a premier project with one of the world's top AI labs.
In this role, you will contribute your subject matter expertise to a cutting-edge project involving frontier large language models. Specifically, you will craft high-quality, challenging problems in your domain expertise with real-world applicability to inform the future of AI innovation.
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