Matthew Bowyer Analytics and Data Science Manager

Capabilities

Skills & Capabilities

Technical and professional capability, grouped honestly. Every entry points at the module page or artefact on this site that evidences it — nothing here is claimed without a source.

How to read this page

These are the capabilities evidenced by the supplied site and by the academic artefacts in this portfolio. My professional context adds more than a coursework portfolio can show; I would rather discuss that in conversation than pad this page with claims the evidence here cannot support.

  • Languages & Tools

    The tooling used day to day, evidenced on this site and in the artefacts.

  • Data & Analytics

    Turning data into a decision, with the statistical grounding to defend it.

    • Data Science Stated on the supplied site and evidenced across every module in this portfolio.
    • Business intelligence Stated on the supplied site; reflected in the reporting and dashboard work across the programme.
    • Applied statistics Hypothesis testing, summary measures, linear and generalised linear models, time series and Bayesian methods in Practical modern statistics and Applied statistical modelling.
    • Data wrangling Cleansing and normalisation of large, diverse datasets in Deciphering Big Data.
    • Data visualisation and reporting Dashboard design and delivery in Visualising Data — see the Unit 12 dashboard manual.
    • Automation Stated on the supplied site as reporting automation; the same instinct runs through the programming and pipeline work in the MSc.

  • Machine Learning & AI

    Building models, evaluating them honestly, and knowing their limits.

    • Machine learning A full twelve-unit reflective module in Machine Learning: exploratory analysis, regression, clustering with scikit-learn and neural networks.
    • Artificial intelligence Deep neural learning, convolutional and recurrent networks in Machine Learning and Artificial Intelligence.
    • Neural networks Creating, training and evaluating neural networks — evidenced by the Unit 11 CNN dashcam presentation.
    • Applied AI in telematics The supplied site states telematics and applied artificial intelligence as specialisms; the Unit 11 project applies CNNs to dashcam footage.
    • Research and literature review A thirteen-page churn-prediction literature review and a full research proposal, in Research Methods and Professional Practice.

  • Cloud & Engineering

    Enough infrastructure knowledge to get analysis into production.

Capabilities from the current CV

The CV groups my capability into three areas. The wording below is the CV’s own, each tied to where it is evidenced on this site or in the roles.

  • Leadership & Strategy

    Analytics strategy, data strategy, AI/ML capability building, executive decision support, stakeholder management, cross-functional delivery, mentoring.

    Evidenced by the Manager: Data Analytics & Global Reporting role at Cartrack (Experience).

  • Data Science & AI

    Python, SQL, machine learning, statistical modelling, predictive & prescriptive analytics, decision science, optimisation, feature engineering & selection, experimentation, hypothesis testing, model validation, tuning, calibration, performance benchmarking, GenAI, LLMs, RAG, VLMs.

    Evidenced across the Cartrack, Greendoor Group and Smith Yong roles, the MSc modules, and the thesis demonstration.

  • Engineering & Platforms

    Databricks, MLflow, Spark, PySpark, Delta Lake, Feature Store, MLOps, ETL/ELT, data pipelines, data modelling, model monitoring & drift, data/model governance, data quality, lineage, AWS, MySQL, Tableau.

    Evidenced by the Engineering & Platforms group on the CV and the data-engineering modules in this portfolio.

Where this is evidenced

Every capability above traces to the programme pages, module pages or downloadable artefacts below.

Programme

Artefacts

The Evidence surface lists every report, literature review, presentation and worksheet produced across the programme, with attribution.