Philip Spanoudes

• Led a team of 5 MLEs delivering predictive models for seller growth across Lifecycle Marketing, Account Management, and Customer Support; owned roadmap, prioritization, and delivery.
• Increased churn model precision by 20% and reduced false positives, improving intervention targeting and enabling new retention strategies in lifecycle and account management.
• Established a cross-functional churn working group for monitoring, prioritization, and rapid iteration on mitigation strategies.
• Built an LLM-powered Merchant Insights Platform using map-reduce + RAG to synthesize emails, surveys, and call transcripts into actionable insights; enabled the development of dashboards for key business questions. Platform included a conversational agent for querying insights in ad-hoc, supporting account management in understanding merchant journeys, issues, and recommended talking points.

• Tech lead, Square Measurement Science: owned forecasting for internal metrics used in quarterly/annual planning and portfolio optimization across Square.
• Formed an independent MLE group and co-designed a framework to deploy trained ML models as services, improving reliability and time-to-production.
• Enhanced merchant value forecasting to incorporate observed and hypothesized COVID-19 effects, improving planning fidelity under regime change.
• Built a training framework for predictive models forecasting merchant value across Square products.
• Improved the merchant churn model by reducing false positives in the upper score band, increasing relevant case generation for retention programs.

• Early ML team member focused on underwriting/line sizing for the Titan charge card and bootstrapping ML infrastructure and standards.
• Evaluated and vetted third-party data partners for underwriting and line sizing, balancing predictive lift, coverage, and compliance.
• Delivered ML tooling adopted across the data team; implemented daily user signals (transactions, balances) for monitoring and predictive modeling.

Part of the initial Square Capital Data Science team that was tasked with the continual optimization of all lending product design, marketing strategies and servicing techniques.

• Optimized IRR forecasting for Capital Flex to improve capital deployment decisions and unit economics.
• Built a product simulation framework to evaluate model swaps and loan facilitation methodology changes before production rollout.
• Implemented heuristic optimization for eligibility thresholds, balancing expected loss and volume against business objectives.
• Developed an email servicing model providing automated solution suggestions for customer inquiries.
• Built a Capital acceptance model using merchant event patterns to predict product acceptance and improve targeting.

• Principal research scientist improving client churn prediction across diverse event data sources.
• Designed a generalized ML pipeline for arbitrary customer churn prediction and an extensible data representation architecture for heterogeneous event streams.
• Implemented a deep learning architecture that decomposed complex user event patterns, increasing prediction accuracy across clients.

Best Overall Student Performance Award (top performance across degree modules).

Developed a novel deep learning architecture that fuses behavioral signals with email content to automatically suggest solutions to customer inquiries; research conducted at Square Capital.

MSc dissertation research (Lancaster University performed for Framed Data) demonstrating that deep learning can outperform traditional ML in churn prediction while reducing manual feature engineering effort.