Description

Apply causal inference and machine learning to estimate the impact of marketing interventions and product strategies on e-commerce KPIs.

Responsibilities

• Develop and implement causal inference methodologies including propensity score matching, instrumental variables, and difference-in-differences.
• Conduct incrementality analysis to distinguish between correlated and causal effects on sales, acquisition, and retention.
• Build and optimize machine learning models for uplift modeling, heterogeneous treatment effect estimation, and causal forests.
• Design and implement A/B tests and experimental frameworks to measure the impact of new features and promotions.
• Analyze large-scale datasets from transaction and user behavior sources to generate actionable insights.

Required Skills

• 7+ years of hands-on experience in causal inference and machine learning within e-commerce or retail.
• Expertise in causal techniques: propensity score matching, instrumental variables, synthetic control, regression discontinuity, and uplift modeling.
• Proficiency in Python or R using libraries such as DoWhy, CausalML, EconML, or causalImpact.
• Strong command of SQL, Spark, and Hadoop for processing large datasets.
• Deep understanding of experimental design, A/B testing, and incrementality measurement.
• Advanced statistical knowledge including Bayesian methods and counterfactual modeling.
• Bachelor's degree in Computer Science (Master’s or Ph.D. in Statistics, Econometrics, ML, Data Science, Economics, or related quantitative field preferred).

Preferred Skills

• Experience with cloud platforms like AWS, Google Cloud, or Azure for model deployment.
• Experience with Bayesian causal inference and heterogeneous treatment effect estimation.
• Contributions to research papers or conference presentations in causal machine learning or econometrics.

Key Skills

Education

Bachelor's degree in Computer Science