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Jordan Clive has a diverse work experience in the fields of deep learning, research, data science, and machine learning. In 2021, they began working as a Deep Learning Engineer at Chattermill, where they build deep learning models for NLP, taking into consideration the entire ML pipeline. Jordan is also a Research Post Masters at Imperial College London, where they are the first author of a paper introducing a state-of-the-art method to adapt large language models for text generation. In 2019, they worked as a Data Scientist at Funding Circle UK, where they were responsible for model validation for credit risk models, as well as feature engineering and training gradient boosting models on tabular data for the Fraud team. Jordan also worked as a Freelance Data Scientist for Novum Insights and GovernorHub. In 2018, they completed a 12-week Data Science Immersive course at General Assembly, covering a broad range of ML topics.

Jordan Clive has a diverse educational background. From 2013 to 2017, they obtained a B.Sc. in Mathematics and Earth Sciences (Natural Sciences) from Durham University. In 2018 and 2019, they attended General Assembly and completed a 12-week Data Science Immersive program. Currently, they are attending Imperial College London, where they are pursuing an MSc Computing (Machine Learning and Artificial Intelligence). In addition to their formal education, they also have a few certifications. Jordan obtained a Structuring Machine Learning Projects certificate from Coursera in September 2019, a R Programming certificate from Coursera in May 2019, and a Duolingo French Fluency: Intermediate (Estimated) certificate from Duolingo in September 2016.