Daniel Cook
Head of Data Science @Moneyhub
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WORK HISTORY
Head of Data Science @Moneyhub
Bristol, GB
Responsible for the organisation\'s machine learning based transaction categorisation engine, quantitative analysis of consumer spending and development of statistical techniques to approve mortgage applicants based on affordability criteria.Python, Numpy, Scikit-learn, Pytorch, AWS SageMaker and Athena are commonly used. Fluent in investigating initial ideas through to completing the Machine Learning Engineer elements of the role taking prototypes through to production.
EDUCATION
University of Bath
MSc, Data Science
University of York
BEng, Computer Science
ABOUT DANIEL COOK
Specialist in creating intellectual property for scale-ups with current and past projects achieving seven figure annual recurring revenue.For my current employer Moneyhub, sole designer of the data enrichment stack with the majority of the implementation delivered by myself to:* Categorise transactions from Open Banking into expenditure and income categories using a ML model.* Detect the retailer/brand on transactions with >99% precision in the presence of noise/stopwords.* Identify regular income and payments: not just direct debits and standard orders but recurring card payments for subscriptions such as Netflix.* Geotag transactions including resolving ambiguity: e.g. identifying the specific McDonald\'s when the description is just \"McDonald\'s Bristol\".* Scale to process in excess of 300 million transactions per day.For a previous employer, Living Map, creator of an indoor positioning system guiding passengers through airports. This leveraged machine learning to integrate WiFi signals, gyroscope, and accelerometer data, achieving indoor location prediction accuracy better than 5 meters.
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