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Co-Founder & CTO @ Lightdash - Agentic BI
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University of Southampton
Doctor of Philosophy (PhD), Stochastic Modelling in Nanomagnetism
January 1, 2013 – January 1, 2018
The University of Sheffield
MEng, Aerospace Engineering
January 1, 2009 – January 1, 2013
Farnborough 6th Form College
A Levels, Maths, Further Maths, Physics, Electronics, Music Technology
January 1, 2007 – January 1, 2009
Lightdash
Co-Founder & CTO
February 1, 2021 – Present
Greater London, England, United Kingdom
Stack More Layers
Machine Learning Engineer
December 1, 2019 – January 1, 2021
Greater London, England, United Kingdom
Cytora
Machine Learning Scientist
February 1, 2018 – December 1, 2019
Cytora
Data Scientist
August 1, 2016 – February 1, 2018
Soton Quants
Meetup Founder
August 1, 2016 – December 1, 2017
Greater Southampton Area
Cytora
Data Scientist (intern)
April 1, 2016 – August 1, 2016
University of Southampton
Learning Support Assistant
November 1, 2013 – August 1, 2014
Enabling Services
University of Sheffield
Research Assistant
June 1, 2013 – November 1, 2013
Automatic Control & Systems Engineering
University of Sheffield
Undergraduate Researcher
May 1, 2012 – July 1, 2012
Amy Johnson Building
University of Sheffield
Research Assistant
June 1, 2011 – September 1, 2011
Rolls Royce University Technology Centre, Amy Johnson Building
MEng Dissertation - Final Year Project
May 1, 2013 – Present
Undergraduate final year project "Predicting the Remaining Useful Life of Gas Turbine Engines Using Kalman Filtering Techniques" also supported the 'Prognostics and Health Management' research currently being undertaken in the 'Rolls Royce University Technology Centre'. ABSTRACT: Condition-based maintenance strategies in the aerospace industry rely on accurate predictions of the remaining useful life of an asset in order to optimise maintenance scheduling, resources and supply chain management. Due to the substantial costs involved, small improvements in efficiency could have significant cost reductions for maintenance services. Failure models for gas turbine engines are not currently available and therefore remaining useful life must be determined from condition monitoring data. This paper investigates the suitability of the Kalman filter as a framework for data-driven degradation models. Its performance is benchmarked against current methods used in industry and demonstrates superior predictions and also provides invaluable confidence information.
Cultural Fit Analysis
The candidate's diverse experience, ranging from academic research to startup CTO and freelance ML engineering, indicates a strong adaptability and willingness to engage with various challenges. Their involvement in founding a meetup (Soton Quants) and co-founding a startup (Lightdash) demonstrates initiative, community engagement, and an entrepreneurial spirit. The focus on open-source BI at Lightdash aligns with collaborative and transparent work environments. While the target role is 'Data Analyst', the candidate's background is heavily skewed towards Machine Learning and Data Science, which might indicate a potential overqualification or a desire for more advanced analytical roles. The breadth of skills and project diversity suggest a candidate who thrives in dynamic, intellectually stimulating environments.
Soft Skills & Operational Fit
The candidate's experience as a Co-Founder & CTO, Tech Lead, and Meetup Founder suggests strong leadership, initiative, and communication skills. Their freelance work indicates adaptability and project management capabilities. The academic background and research roles highlight problem-solving and critical thinking. The 'Learning Support Assistant' role suggests empathy and teaching abilities. Overall, the candidate appears to be a proactive, technically strong individual with a blend of academic rigor and practical application.