Role Overview
We are looking for a Data Scientist who can hit the ground running immediately in commercial measurement and causal analytics. The ideal candidate must go beyond standard machine learning modeling to demonstrate expertise in experimental design, statistical measurement, and observational causal inference.
Key Technical Requirements & Skills
- Core Focus: Causal Analytics, Lift Measurement, and Impact Attribution (rather than pure Predictive Machine Learning).
- Experimental Design & A/B Testing:
- Statistical power calculations, sample size determination, and Minimum Detectable Effect (MDE).
- In-depth understanding of A/B test mechanics and variance reduction techniques.
- Quasi-Experimental & Observational Causal Inference:
- Difference-in-Differences (DiD)
- Synthetic Controls
- Propensity Score Matching (PSM)
- Regression Discontinuity Designs (RDD)
- Uplift & Behavioral Attribution:
- Demonstrated experience proving that a specific model, campaign, or feature drove a true incrementality/change in user or customer behavior.
- Hands-on experience with uplift modeling techniques.
- Technical Stack:
- Strong proficiency in Python, specifically statistical packages such as statsmodels, scipy.stats, CausalPy, or DoWhy
