Tech Stack

Description

You will lead the experimentation roadmap, design and analyze complex A/B tests, apply causal inference techniques, and conduct deep-dive analyses to uncover user behavior and metric drivers. You'll also develop measurement frameworks, mentor junior data scientists, and translate technical findings into strategic recommendations for leadership.

Requirements

• 6+ years experience in applied data science, economics, or product analytics with a focus on experimentation and causal inference
• Bachelor's degree in a quantitative field; Master's preferred
• Expertise in causal inference methods (quasi-experiments, matching, DiD, IV)
• Advanced proficiency in Python or R for statistical modeling (scikit-learn, numpy, pandas)
• Expert-level SQL and experience with data warehouses (Snowflake, Postgres)

Responsibilities

• Define and execute the experimentation roadmap focusing on customer workflows, churn risk, and LTV
• Design, implement, and analyze A/B tests and multivariate experiments using Bayesian methods
• Apply causal inference techniques (difference-in-differences, synthetic control, propensity score matching, instrumental variables) where RCTs are infeasible
• Conduct exploratory analysis to discover latent user behavior and root causes of metric changes
• Define and govern a unified KPI framework mapping product health to business outcomes