Timothy Williams
About Timothy Williams
Timothy Williams is a Data Science Team Lead at Trainline, with a background in physics and extensive experience in data science. He previously worked at CERN, where he made significant contributions to particle physics research, and has held various roles in data science across multiple organizations.
Work at Trainline
Timothy Williams currently serves as the Data Science Team Lead at Trainline, a position he has held since 2020. In this role, he has led the development of innovative product features, including price prediction, split tickets, and crowd alerts, leveraging data and machine learning techniques. Prior to his current position, he worked as a Senior Data Scientist from 2019 to 2020 and as a Data Scientist from 2018 to 2019. His contributions have included optimizing business-critical infrastructure using Gradient boosted decision trees, which resulted in significant cost savings and revenue gains.
Education and Expertise
Timothy Williams studied at the University of Birmingham, where he achieved a Master of Science (MSci) in Physics from 2010 to 2014. He continued his education at the same institution, earning a Doctor of Philosophy (PhD) in Experimental Particle Physics from 2014 to 2018. His academic background has equipped him with a strong foundation in both Bayesian and frequentist statistical methods, which he utilized to evaluate the outcomes of A/B tests in his professional roles.
Background
Before his tenure at Trainline, Timothy Williams worked at CERN as a PhD Researcher from 2014 to 2018. During this time, he was part of the LHCb collaboration, which consisted of 800 physicists. He contributed to significant research, including the world's first observation of a rare physical process at the Large Hadron Collider (LHC). Additionally, he served as a Summer Research Student at Rutherford Appleton Laboratory in 2013 for two months.
Achievements
Timothy Williams has made notable contributions to the field of data science and physics. At CERN, he was involved in groundbreaking research that led to the world's first observation of a rare physical process. At Trainline, he played a key role in developing innovative product features and optimizing infrastructure, demonstrating his ability to apply data science techniques to real-world problems. His work has had a measurable impact on the efficiency and effectiveness of the projects he has been involved in.