Philip Spanoudes

• Architected and built ML/AI infrastructure and client-facing AI agent sales platform from the ground up.
• Engineered proprietary LLM-powered agents for automated lead qualification, directly generating net-new revenue for sales teams.
• Achieved ~$500K ARR with a proven product-market fit, scaling the platform to support major enterprise deals & adoption.
• Directed all technical strategy and roadmap execution, leading the company through initial build, launch, and strategic growth phases.

• Led a team of 5 MLEs delivering predictive models for seller growth and risk-adjacent life-cycle marketing
• Increased churn model precision by 20% and reduced false positives in upper score bands, directly improving intervention targeting and revenue retention.
• Established cross-functional working groups for real-time monitoring and rapid iteration of mitigation strategies.
• Built an LLM-powered Merchant Insights Platform using Map-Reduce and RAG to synthesize multi-modal data (emails, transcripts) into actionable insights; reducing call preparation time for Account Management and Sales teams.

• Tech lead, Square Measurement Science: owned forecasting for internal metrics used in quarterly/annual planning and portfolio optimization across Square.
• Co-designed a framework to deploy trained ML models as services, significantly improving reliability and time-to-production for the organization.
• Enhanced merchant value forecasting to incorporate observed and hypothesized COVID-19 effects, improving planning fidelity under regime change.
• Developed training frameworks for predictive models forecasting merchant value across all Square products.

• Part of the foundational Square Capital ML team; contributed to the design, development and improvement of credit risk models used for underwriting.
• Optimized heuristic eligibility thresholds for lending, balancing expected loss against volume objectives.
• Built a comprehensive product simulation framework to evaluate model swaps and loan facilitation changes prior to production rollout.
• Developed a Capital acceptance model utilizing merchant event patterns to predict product uptake and improve targeting.
• Built an email servicing model providing automated solution suggestions based on behavioral signals.

• Bootstrapped ML infrastructure and standards for underwriting and line-sizing for the Titan charge card
• Evaluated third-party data partners for underwriting, balancing predictive lift with compliance and coverage.
• Implemented daily user signals (transactions, balances) for real-time monitoring and predictive modeling.

• 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.