Charlie Bickerton
About Charlie Bickerton
Charlie Bickerton is the Lead Data Scientist at Carv, where he manages applied data science projects and leads a team focused on context detection and movement analysis. He co-founded a data science consultancy and has held previous roles at Miminal and the University of Bristol.
Current Role as Lead Data Scientist
Charlie Bickerton currently serves as the Lead Data Scientist at Carv, a position he has held since 2022. In this role, he leads a team that focuses on context detection, movement analysis, and coaching optimization utilizing sensor data. His responsibilities include managing applied data science projects that align with Carv's broader business objectives. His expertise in data science contributes to the development of innovative solutions within the company.
Previous Experience at Carv
Before becoming the Lead Data Scientist, Charlie Bickerton worked at Carv as a Data Scientist from 2019 to 2022. During his three years in this role, he contributed to various data-driven projects aimed at enhancing the company's offerings. His work involved analyzing data to improve product performance and user experience.
Experience at Miminal
Charlie Bickerton held the position of Director & Data Scientist at Miminal from 2017 to 2019. In this role, he was responsible for overseeing data science initiatives and integrating data-driven solutions into business processes. His leadership at Miminal helped shape the company's approach to data science and analytics.
Research Background at University of Bristol
Prior to his industry roles, Charlie Bickerton worked as a Research Associate at the University of Bristol from 2017 to 2018. This position allowed him to engage in academic research, contributing to the field of data science and analytics. His experience in academia provided a strong foundation for his subsequent work in the industry.
Education in Engineering Mathematics
Charlie Bickerton studied at the University of Bristol, where he earned a Master of Engineering (MEng) in Engineering Mathematics from 2013 to 2017. This educational background equipped him with the analytical and mathematical skills necessary for a successful career in data science.