Monzo's Machine Learning Stack
👋 Neal is currently the Machine Learning Lead at Monzo in London, where they’re focusing on building machine learning systems that optimize the app and help the company scale.
✈️ Before joining Monzo, Neal was a Data Scientist at Skyscanner, where he built recommender and ranking systems to improve travel information in the app.
🏫 Before Skyscanner, Neal was a Senior Research Associate in the Computer Lab at the University of Cambridge, working on healthcare mobile apps that use smartphone sensors. I spun out this research into a startup that was part of Accelerate Cambridge in the Judge Business School.
🎓 Neal did his MSci (in Computer Science), Ph.D. (on recommender systems), and first postdoctoral research position (on Urban Data Science) in the Department of Computer Science at University College London, where he's still an Honorary Research Associate.
Neal's Ph.D. focussed on methods for evaluating collaborative filtering algorithms over time. While at UCL, Neal also spent time as a visiting researcher at Telefonica Research, Barcelona, and worked as a Data Science consultant.
Neal's work has always focused on applications that use machine learning - this has taken him from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, and banking. You can read more about his work and research in the Press & Speaking and Research sections.
👋 Neal is currently the Machine Learning Lead at Monzo in London, where they’re focusing on building machine learning systems that optimize the app and help the company scale.
✈️ Before joining Monzo, Neal was a Data Scientist at Skyscanner, where he built recommender and ranking systems to improve travel information in the app.
🏫 Before Skyscanner, Neal was a Senior Research Associate in the Computer Lab at the University of Cambridge, working on healthcare mobile apps that use smartphone sensors. I spun out this research into a startup that was part of Accelerate Cambridge in the Judge Business School.
🎓 Neal did his MSci (in Computer Science), Ph.D. (on recommender systems), and first postdoctoral research position (on Urban Data Science) in the Department of Computer Science at University College London, where he's still an Honorary Research Associate.
Neal's Ph.D. focussed on methods for evaluating collaborative filtering algorithms over time. While at UCL, Neal also spent time as a visiting researcher at Telefonica Research, Barcelona, and worked as a Data Science consultant.
Neal's work has always focused on applications that use machine learning - this has taken him from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, and banking. You can read more about his work and research in the Press & Speaking and Research sections.
Neal Lathia, a staff ML engineer at Monzo, provides an overview of Monzo's journey from a prepaid card to a fully-fledged bank with over 7 million customers. Lathia shares his experience of building Monzo's ML platform and team and highlights some of the challenges they faced. He explains how Monzo's ML stack is built on top of their existing engineering and analytics foundations, and how they developed an ML Ops path to enable the deployment of ML models in a flexible and reliable way. Lathia also discusses the ML frameworks used by Monzo and the importance of speed and determinism in ML when dealing with transaction data.