Isabelle Tilsley
About Isabelle Tilsley
Isabelle Tilsley is a Sales Engineer at Egress Software Technologies, with a background in mechanical engineering and artificial intelligence. She has held various roles in customer service and office administration, and has conducted research on explainable AI.
Current Role at Egress Software Technologies
Isabelle Tilsley currently serves as a Sales Engineer at Egress Software Technologies, a position she has held since 2023. In this role, she leverages her technical background and communication skills to support sales initiatives and enhance customer engagement. Her responsibilities include understanding client needs, presenting product solutions, and facilitating the sales process.
Previous Work Experience
Before joining Egress, Isabelle held various positions that contributed to her professional development. She worked as a Receptionist at KPI Recruiting Ltd from 2018 to 2022 for four years. Prior to that, she was an Information Advisor at Wren Kitchens for seven months in 2020. Additionally, she gained experience as Office Staff at BHygienic for three months in 2017, and as a Bartender at Peppermint Bar & Kitchen for five months in 2019. Isabelle also worked as a Sales Assistant at Starbucks from 2017 to 2021.
Educational Background
Isabelle Tilsley studied at Swansea University, where she earned a Bachelor of Engineering (BEng) in Mechanical Engineering from 2018 to 2021. She continued her education at the same institution, obtaining a Master of Science (MSc) in Human-Centred Big Data and Artificial Intelligence from 2021 to 2022. Prior to her university studies, she completed A Levels in Maths, Physics, and Chemistry at Newcastle-under-Lyme School from 2011 to 2018.
Research and Specialization
Isabelle conducted research focused on explainable AI, specifically on transparency mechanisms and XAI models aimed at enhancing trust in machine learning systems. She specialized in developing communication methods for AI explainability, tailoring these methods to various social parameters such as age and social setting. This research reflects her transition from mechanical engineering to computer science, driven by her interest in AI algorithm development and technological advancements.