Ian Cappellani

Ian Cappellani

Software/Ml Engineer, Anti Abuse @ Faire

About Ian Cappellani

Ian Cappellani is a Software and Machine Learning Engineer specializing in anti-abuse measures. He has a background in software development and data analysis, with experience at Faire and Code for Canada, and holds an Honours Bachelor of Science from the University of Toronto.

Work at Faire

Ian Cappellani has held multiple roles at Faire since 2021. He started as a Software Engineer L2 (Backend) for 7 months before advancing to Software Engineer L3 (Backend) for 5 months in 2022. Currently, he serves as a Software/ML Engineer in the Anti-Abuse team, a position he has held for 2 years. In his roles, he has focused on optimizing backend processes and enhancing security measures. His contributions include designing a system that optimized ad targeting strategies, resulting in significant annual savings.

Education and Expertise

Ian Cappellani studied at the University of Toronto, where he earned an Honours Bachelor of Science with High Distinction. He completed a double major in Computer Science and Philosophy from 2015 to 2020. His educational background equips him with a strong foundation in technical and analytical skills, which he applies in his current role in software and machine learning engineering.

Background

Before joining Faire, Ian Cappellani worked as a Developer Fellow at Code for Canada from 2020 to 2021. He also gained experience as an Engineering Intern at Hansen Technologies in Toronto, Ontario, from 2018 to 2019. These roles provided him with practical experience in software development and engineering, contributing to his professional growth in the tech industry.

Achievements

Ian Cappellani has made significant contributions in his roles, particularly at Faire. He performed optimizations that reduced the p50 login endpoint latency by 71%. Additionally, he trained and deployed a gradient boosting model that increased sales by over $5M annually by reducing login friction. He also proposed and implemented real-time heuristics for detecting bots and fraudulent transactions, enhancing the company's security posture.

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