Philip Spanoudes

Tech lead on the Square Measurement Science team. The team is responsible for all forecasted internal metrics that are utilized from multiple teams across Square for quarterly/annual planning and investment portfolio optimization.

• Consulted on the design & development of various projects within the team. Helped by identifying possible gaps/roadblocks and suggesting ways on how to overcome these risks.
• Formed a small independent team of MLE's within Square and contributed to the design and implementation of a framework for deploying trained ML models as a service.
• Enhanced existing merchant value forecast models to take into account the observed effects as well as speculated effects of the COVID-19 pandemic.
• Built a framework that facilitates the training of predictive models that are used to forecast a merchant's value across various Square products.
• Improved merchant churn prediction model, significantly decreasing the false positive rate in the upper score range. This in turn increased the amount of relevant case generation for the merchant retention program.

Part of the initial ML team at Point, specifically hired to help build out the line sizing methodology for the upcoming charge card product (Titan), as well as to help kickstart the ML infrastructure and broader ML standards at Point.

• Consulted on the selection and vetting of potential third party data partners that would ultimately be used to facilitate the underwriting and line sizing methodologies for the upcoming charge card product.
• Designed and built ML tooling that was actively utilized by the rest of the data team at Point.
• Implemented various daily user related features (signals), specifically around transactions and account balances that were used for internal monitoring and development of predictive models.

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.

• Improved the internal rate of return (IRR) forecasting model for the Capital Flex product.
• Designed and implemented a product simulation framework that is used to evaluate the effects of model swaps and loan facilitation methodology alterations.
• Designed and implemented a heuristic optimization framework for product eligibility that searches for optimum threshold configurations based on expected loss and volume.
• Designed and implemented an email servicing model for automated solution suggestions to email inquiries.
• Designed and implemented a Capital acceptance model for the marketing team that uses merchant event patterns to determine the probability of product acceptance.

Hired as Framed Data's principal research scientist with the sole task of improving the company's churn prediction algorithms for each customer.

• Researched and implemented a novel machine learning pipeline for arbitrary customer churn prediction.
• Invented a generalized data representation architecture that can be applied on different raw event company data.
• Implemented a state-of-the-art Deep Learning architecture that effectively decomposed complex user event patterns and ultimately increased prediction accuracies.

Best Overall Student Performance Award: Prestigious award in recognition for best student performance across all degree modules.

Research conducted at Square Capital for the purpose of automatically suggesting solutions to customer email inquiries. The research yielded a novel deep learning architecture that combines two disparate data sources when estimating its predictions.

Initial research performed for Framed Data as part of the MSc Data Science Degree dissertation at Lancaster University. The research work conducted proved that Deep Learning can be successfully applied in the field of customer churn prediction by yielding better prediction results while also bypassing the tedious feature engineering phase in a traditional machine learning pipeline.