Oscar Barlow
About Oscar Barlow
Oscar Barlow serves as the Data Practice Lead at Starling Bank, where he has implemented drift detection on machine learning models and launched a machine learning explainability research project. He holds a BA in Philosophy from the University of Nottingham and an MS in International Marketing from Birkbeck, University of London.
Work at Starling Bank
Oscar Barlow currently serves as the Data Practice Lead at Starling Bank, a position he has held since 2023. In this role, he focuses on enhancing the bank's data practices and machine learning capabilities. Previously, he worked as a Tech Lead and Engineering Manager at Starling Bank from 2021 to 2023. During his tenure, he implemented drift detection on machine learning models in production and launched a machine learning explainability data science research project. He also created an 'accessible ML' framework to promote machine learning initiatives within the organization and successfully shipped the bank's first entirely cloud-trained models.
Education and Expertise
Oscar Barlow studied at the University of Nottingham, where he earned a Bachelor of Arts with Honours in Philosophy from 2005 to 2008. He furthered his education at Birkbeck, University of London, obtaining a Master of Science in International Marketing from 2012 to 2014. His academic background supports his expertise in data science and machine learning, particularly in the financial sector.
Background
Before joining Starling Bank, Oscar Barlow worked at Makers Academy as a Developer for a brief period in 2017. His experience in software development laid the foundation for his later roles in data and engineering management. His career trajectory reflects a strong focus on technology and data-driven solutions within the banking industry.
Achievements
Throughout his career at Starling Bank, Oscar Barlow has made significant contributions to the bank's machine learning capabilities. He implemented drift detection on machine learning models, which is crucial for maintaining model performance over time. Additionally, he launched a research project focused on machine learning explainability, addressing the need for transparency in AI systems. His creation of an 'accessible ML' framework has facilitated increased machine learning activities within the organization.