Mammoth Growth is seeking to accelerate testing velocity for SEO initiatives by executing multi-cell tests using established causal impact frameworks for Fortune 500 companies
Requirements
- Proficient in Python (pandas, numpy, statsmodels) or R
- Experience with massive datasets - TB scale
- Strong SQL skills for data extraction and manipulation
- Statistical foundations: experimental design, hypothesis testing, regression analysis, time series basics
- Experience with A/B testing or incrementality testing (minimum 10+ tests executed)
- Familiarity with causal inference methods (synthetic controls, matched markets, difference-in-differences, causal impact)
- Experience with BI platforms (Looker, Tableau, Mode, or similar)
Responsibilities
- Execute incrementality tests using established causal impact frameworks (Google CausalImpact, synthetic controls, matched markets)
- Run multiple tests per week to determine statistically significant wins for SEO initiatives
- Design and analyze matched market experiments to isolate SEO impact from confounding factors
- Implement statistical power analysis to optimize test design and reduce time-to-results
- Translate test results into clear, actionable recommendations for SEO roadmap decisions
- Monitor SEO performance metrics and surface meaningful performance deviations
- Build repeatable analytical workflows that standardize test execution
Other
- 4+ years analyzing digital marketing performance (SEO, paid search, e-commerce, or similar channels)
- Hands-on experience executing tests at volume - comfortable running multiple tests per week/month
- Understanding of e-commerce metrics, conversion funnels, and attribution
- Client-facing or embedded team experience (consulting, agency, or similar environments)
- Clear communicator who can explain statistical concepts to non-technical stakeholders
