Alistair Sturgiss
About Alistair Sturgiss
Alistair Sturgiss is a Data Scientist with a background in Chemistry, having studied at Imperial College London. He has experience working at dunnhumby and currently works at LMAX Group in London.
Work at LMAX Group
Alistair Sturgiss has been employed at LMAX Group as a Data Scientist since 2020. His role involves leveraging data analysis to support business decisions and enhance operational efficiency. LMAX Group is known for its innovative approach in the financial services sector, focusing on providing a transparent and efficient trading environment. Sturgiss contributes to the company's data-driven initiatives, utilizing his expertise to analyze complex datasets and derive actionable insights.
Education and Expertise
Alistair Sturgiss completed his secondary education at Newcastle Royal Grammar School, where he achieved A Level results from 2013 to 2015. He then pursued higher education at Imperial College London, earning a Bachelor of Science degree in Chemistry with First Class Honours from 2015 to 2018. His academic background provides a strong foundation in analytical thinking and problem-solving, which he applies in his current role as a Data Scientist.
Background in Data Science
Prior to his current position at LMAX Group, Alistair Sturgiss worked at dunnhumby as an Applied Data Scientist for 11 months in 2019. During his tenure in London, he focused on applying data science techniques to enhance customer insights and drive business strategies. This experience helped him develop practical skills in data analysis and modeling, which he has further utilized in his ongoing work at LMAX Group.
Professional Experience
Alistair Sturgiss has accumulated significant professional experience in the field of data science. His role at dunnhumby provided him with insights into customer behavior and data-driven decision-making. Since joining LMAX Group in 2020, he has continued to expand his expertise in data analysis, contributing to various projects aimed at optimizing trading processes and improving data management practices.