Job Summary:
This senior role leads complex statistical and analytical initiatives that drive omni-channel growth, loyalty program performance, personalization strategy, and digital marketing optimization. You will apply advanced modeling techniques and experimentation frameworks to shape customer experiences and influence strategic decisions across grocery retail and eCommerce.
Role and Responsibilities:
Advanced Statistical Modeling & ML Support:
- Lead sophisticated modeling initiatives, including:
- Multivariate and logistic regression for driver analysis
- Survival analysis for churn forecasting
- Uplift modeling for retention and offer targeting
- Marketing attribution and MMM (Marketing Mix Modeling)
- Clustering using advanced approaches (mixture models, DBSCAN)
- Develop features and oversee model evaluation using statistical rigor (AUC, lift curves, ROC).
Customer & Marketing Science Leadership:
- Drive customer lifecycle and behavioral analytics across acquisition, activation, loyalty engagement, and retention.
- Lead design and analysis of personalization routines (next-best-action, recommendation ranking, offer targeting).
- Conduct promotional and pricing optimization analyses using elasticity modeling and constrained optimization techniques.
Experimentation Strategy & Causal Methods:
- Oversee A/B and multivariate experimentation programs using advanced causal inference (CUPED, diff-in-diff, synthetic controls).
- Provide experimentation strategy to product managers and marketing leads, ensuring strong statistical governance.
Omnichannel & eCommerce Insights:
- Analyze digital shopper journeys across search, product discovery, cart behavior, and fulfillment paths.
- Integrate insights from store, digital, loyalty, and marketing channels to provide executive-level recommendations.
Leadership & Influence:
- Present insights to senior leadership, influencing eCommerce, merchandising, and loyalty strategy.
- Mentor and manage junior/advanced analysts on statistical methods, experimentation design, and storytelling.
- Establish best practices and analytical standards across the team.
Required Experience:
- Bachelors or masters degree in a quantitative field such as Statistics, Data Science, Applied Mathematics, or Computer Science.
- 46 years of analytics experience with significant exposure to eCommerce, digital marketing, loyalty programs, or omnichannel retail.
- Expert SQL skills and significant experience with large datasets in Snowflake, Databricks, or similar platforms.
- Strong proficiency in Python (pandas, scikit-learn, stats models) for modeling, feature engineering, and evaluation.
- Extensive experience in statistical modeling, time-series forecasting, clustering, and optimization.
- Deep familiarity with marketing analytics (attribution modeling, MMM, incrementality, campaign effectiveness).
- Proven ability to present complex statistical results to executives and guide strategic decisions.