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Monzo Machine Learning Case Study

Posted Dec 07
# FinTech
# Case Study
# Interview
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SPEAKER
Neal Lathia
Neal Lathia
Neal Lathia
Senior Data Science Manager @ Monzo

๐Ÿ‘‹ Neal is currently the Machine Learning Lead at Monzo in London, where theyโ€™re focusing on building machine learning systems that optimize the app and help the company scale.

โœˆ๏ธ Before joining Monzo, Neal was a Data Scientist at Skyscanner, where he built recommender and ranking systems to improve travel information in the app.

๐Ÿซ 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.

๐ŸŽ“ 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.

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๐Ÿ‘‹ Neal is currently the Machine Learning Lead at Monzo in London, where theyโ€™re focusing on building machine learning systems that optimize the app and help the company scale.

โœˆ๏ธ Before joining Monzo, Neal was a Data Scientist at Skyscanner, where he built recommender and ranking systems to improve travel information in the app.

๐Ÿซ 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.

๐ŸŽ“ 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.

+ Read More
SUMMARY

This episode is a deep dive session on Monzo Bank's infra and how they are doing ML. We also touched on some of his greatest learnings over the years while trying to put ML into prod. One of the highlights came when we touched on how they go about assessing if a problem even needs ML in the first place. We also dove into what the structure of the Monzo ML teams look like and how they manage to efficiently collaborate. Consider this the Monzo case study you didn't know you needed.

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