Statistician & Experimentation Expert

What You'll Do5

• Build study scenarios that track real statistical practice, from the design stage through analysis and final reading of results.
• Draft model answers that lay out the reasoning behind every modeling decision you make.
• Review model responses for sound method and for a correct interpretation of the numbers.
• Call out analyses that look rigorous on the surface but rest on invalid conclusions.
• Work inside your specialty, whether that means experimental design, causal inference or A/B testing, and shift weekly hours up or down as project work allows.

Requirements8

• Advanced degree in statistics, biostatistics or a closely related quantitative field.
• Two or more years of applied statistics or experimentation work.
• Depth in experimental design, causal inference or A/B testing.
• Fluency in R or Python for statistical analysis.
• Capacity to commit at least 10 hours per week.
• Preferred: PhD in statistics, biostatistics or econometrics.
• Preferred: experience running large scale experimentation platforms.
• Preferred: Bayesian methods, instrumental variables or synthetic control.

Who Should Apply

The statistician who fits here holds a graduate degree in a quantitative field and has spent two or more years running experiments or causal analyses in practice. You can defend a modeling choice in writing and catch a flawed conclusion buried inside a polished report.

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