Subhankar Banerjee

About Subhankar Banerjee

A data science leader with 10+ years of experience, currently driving subscription growth and engagement strategy at DoorDash as the Tech Lead for the DashPass DS team. Specializing in predictive modeling, experimentation-driven product strategy, causal inference, and uplift modeling, I deliver data products and actionable insights that inform retention planning, CRM initiatives, and key product roadmap decisions. Recent work includes informing retention strategy through behavioral modeling, time series analysis, and Causal ML modeling techniques—enabling personalized onboarding, targeted incentives, and optimized lifecycle interventions that improve customer engagement and retention.Previous roles at LinkedIn and Expedia sharpened expertise in customer acquisition and LTV optimization. Through collaborative leadership across cross-functional teams, consistently delivered high-impact solutions such as deep causal inference, experimentation and LTV modeling that earned executive buy-in and influenced key business decisions. Known for translating complex data into clear, strategic recommendations—with a focus on enabling scalable, cross-functional impact and roadmap-aligned decision-making.

Experience

1. Lead Data Scientist, Dashpass

   DoorDash

   Jun 2022 — Present · Seattle, WA, US

   Leading DS projects across multiple product areas. Expertise in translating behavioral data into product strategy-leveraging predictive modeling, causal inference, and uplift techniques to inform roadmap decisions, retention plays, and CRM initiatives- Defined DashPass retention strategy using predictive modeling and SHAP to surface key churn drivers (Tenure, Savings, Recency, Payment Failure, Defects) and developed actionable recommendations now embedded in CRM and product planning- Built experiment foundations within DashPass and broader consumer org standardizing metrics, experiment analyses and best practices- Uncovered early habituation signals via time series + ML analysis, shaping onboarding and personalized challenges by identifying key behavioral breakpoints in the first few months of subscription- Diagnosed dormancy patterns and built a four-segment win-back playbook that guides in-app nudges and suppresses churn-triggering outreach—now core to dormant lifecycle marketing and the Challenges platform- Advised experimentation platform on CUPAC methods and predictive modeling to improve test sensitivity. Trusted partner for Causal ML strategy across product and analytics- Pioneered causal uplift modeling to optimize promo spend by targeting users with high incremental retention lift—driving smarter ROI on lifecycle incentives- Built a causal prioritization engine using Double Machine Learning ranking retention levers by combining personalized treatment effects with adoption likelihood—powering personalized onboarding, benefit surfacing, and CRM journeys.

Education

• National Institute of Technology Raipur

  B.Tech, Computer Science and Engineering

• Texas A&M University

  MS, Management Information Systems

Skills

• Business Intelligence
• Analysis Services
• Machine Learning
• Sql
• Microsoft Project
• Visio
• Python
• Databases
• Pl/Sql
• R
• Systems Analysis
• Ssis
• Process Improvement
• Data Analysis
• Requirements Gathering
• Ssas
• Data Governance
• Analytical Skills
• Microsoft Sql Server
• Data Warehousing
• Business Requirements
• Analysis
• Predictive Analytics
• Business Objects
• Ms Project
• Ssrs
• Microsoft Excel
• Etl
• Data Quality
• Oracle
• Data Mining
• Sas
• Requirements Analysis
• Sdlc
• Database Design
• Tableau
• Statistical Modeling
• Sharepoint
• Master Data Management
• Data Modeling