Liam Coatman
About Liam Coatman
Liam Coatman is a Software Engineer currently employed at Starling Bank in London, England. He has a diverse background in data science and software engineering, with previous roles at Faculty and New York University Abu Dhabi.
Current Role at Starling Bank
Liam Coatman currently serves as a Software Engineer at Starling Bank, having joined the company in 2022. He has been contributing to the development of software solutions for the bank for a duration of two years. His role involves applying his technical skills to enhance the bank's services and infrastructure.
Previous Experience at Faculty
Liam Coatman has extensive experience at Faculty, where he held multiple roles from 2017 to 2022. He started as a Data Science Fellow for two months in 2017, then transitioned to Data Scientist for nine months in 2019. He later served as a Software Engineer for four months in 2018, followed by a position as Senior Data Scientist from 2019 to 2021 for two years. In 2021, he took on the role of Lead Data Scientist for five months and returned as Lead Software Engineer for nine months in 2022.
International Experience
Liam Coatman has a diverse international background, having worked in various countries. He served as a Global Academic Fellow at New York University Abu Dhabi from 2011 to 2013, contributing to academic programs. Additionally, he held a research position at New York University in 2013. His experience also includes working in the United Kingdom and the United Arab Emirates.
Education and Expertise
Liam Coatman holds a Doctor of Philosophy (PhD) in Astrophysics from the University of Cambridge, which he completed from 2013 to 2017. He also earned a Master of Physics with Honours from The University of Edinburgh between 2007 and 2011. His educational background in physics and astrophysics underpins his analytical and problem-solving capabilities in software engineering and data science.
Transition to Data Science
Liam Coatman transitioned from a research role at New York University to a career in data science, demonstrating adaptability and a diverse skill set. His background in astrophysics informs his approach to data science and software engineering, allowing him to apply complex analytical techniques effectively.