Matthew Bowyer

Background

I am an Analytics and Data Science Manager. My work sits where reporting, operational data and modelling meet: turning complex business requirements into analytical solutions that stakeholders can act on, and automating the manual work that gets in the way of them.

That has meant leading global analytics and reporting delivery for senior stakeholders across finance, operations, telematics, research and development, and executive leadership; managing analysts; and building automated reporting, forecasting, anomaly detection and machine learning applications.

My technical foundation is Python, SQL, Tableau, AWS and data pipeline development. Telematics and operational analytics are the domain I know best, because that is where I have spent most of my career: operational device data and the reporting built on top of it.

Career trajectory

I came into data science from analytics rather than the other way round. The early years were business intelligence and financial and operational reporting — learning what a business actually needs from its numbers, and how much of the value sits in the delivery mechanism rather than the model.

That led into automation: reporting and workflow automation, and developing automated reporting. Forecasting, anomaly detection and machine learning followed naturally, because they were the next questions the business was already asking of data I already had.

The formal underpinning came alongside the job. I completed a BSc (Hons) Data Science with The Open University, and an MSc in Data Science with the University of Essex Online (completed 2026, Merit), while leading an analytics function. Doing both at once has been an education in prioritisation.

What I studied and why

I hold an MSc in Data Science with the University of Essex Online (completed 2026, Merit). The taught modules are in this portfolio — research methods and professional practice, machine learning, deciphering big data, visualising data, the data professional and numerical analysis — on top of the earlier foundation in statistics, mathematics and computing.

My thesis is A Multimodal Driver Behaviour Evaluation System: Combining Telematics, VLMs and LLMs: combining telematics data with vision-language and large language models to evaluate driver behaviour more richly than a single numeric signal allows. It is a direct extension of the work I do professionally, and an interactive demonstration of it is published on the VLM thesis page.

What I am looking for

I want to keep working where analytics has to deliver rather than just analyse: building and leading analytical teams, and putting machine learning and artificial intelligence into production where someone downstream depends on the result being correct.

I am drawn to environments such as motorsport, where high-frequency data, engineering, operational strategy and human performance converge — and more broadly to telematics, sport and applied artificial intelligence, where I have most to offer.

I am open to senior analytics and data science roles, and to consulting enquiries where an organisation needs a reporting and analytics function built, repaired or automated. The fastest way to reach me is email or LinkedIn.

What I have delivered

• Global analytics and reporting deliveryFor senior stakeholders across finance, operations, telematics, research and development, and executive leadership.
• Automated analytics in productionAutomated reporting, forecasting, anomaly detection and machine learning applications.
• Applied researchA multimodal driver behaviour evaluation system combining telematics data, vision-language models and large language models.
• Commercial deliveryCompany and business websites developed and shipped for real clients — see Projects & Modules.

Each of these traces to the supplied site or to a supplied academic artefact. Nothing on this page is asserted beyond that evidence.