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Technology & Computing
Causal Inference Data Scientist
Job Description
Evaluate causal analyses, experiment design, statistical inference, metric construction, and decision recommendations for high-stakes product and policy questions. Review identification strategies, estimands, power assumptions, sampling choices, and uncertainty statements for statistical validity. Identify confounding, selection bias, leakage, post-treatment adjustment, weak counterfactuals, and conclusions that exceed the evidence. Provide reference analyses that connect rigorous methodology to decisions in language non-specialist stakeholders can understand. Remote, project-based contract work. Scope and hours depend on project matching; the listed hourly range applies to matched work.
Why Apply
- Expert-level compensationEarn $140 - $215/hr for work that values your professional judgment.
- Flexible remote workContribute asynchronously around your existing professional commitments.
- Meaningful model impactHelp advanced AI systems reason more accurately in technology & computing.
- Focused projectsAccept only the opportunities that fit your experience and availability.
Responsibilities
- Evaluate advanced domain tasks and AI-generated responses for factual accuracy and professional judgment.
- Apply current standards, methods, and risk considerations from technology & computing.
- Document errors, edge cases, and the reasoning behind an ideal response.
- Collaborate asynchronously with Alpheva's research and evaluation teams.
Required Qualifications
- Graduate degree or equivalent expertise in statistics, economics, biostatistics, or a related quantitative field
- 7+ years applying experimentation, causal inference, or advanced statistical modeling
- Expertise in identification strategy, bias, uncertainty, and communicating limitations
Company Description
Alpheva AI builds the expert data, evaluations, and real-world environments that help frontier AI systems become more capable and reliable. We partner with experienced professionals whose judgment can teach models how high-quality work is actually done.