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Required Skills and Proficiency: •Proficiency in A/B testing, quasi-experiments, causal impact methods (e.g., difference-in-differences, instrumental variables, propensity scoring). •Familiarity with survival/lifecycle modelling, regression, classification, and recommendation systems. •Experience with customer journey analysis, churn prediction, subscription upsell, and personalization algorithms. •Meta-analysis across categories (pooling multiple studies while controlling for heterogeneity). •E-commerce domain knowledge: buy-box dynamics, cart abandonment, returns management, merchandising, trust signals. •Customer-centric thinking: mapping buyer journeys, satisfaction outcomes, and long-term retention vs short-term gains. •Strong SQL and data wrangling (ETL pipelines). •Proficiency with Python/R for statistical computing. •Familiarity with experimentation platforms, BI tools (Tableau, Power BI), and data warehouses (Snowflake, BigQuery, Redshift). •Prior academic research, writing and publishing experience To Apply: Share your prior research works, such as Google Scholar or ORCID