Solihull, England, United Kingdom
Machine learning and AI professional with a passion for uncovering patterns in data and delivering innovative solutions that inform high-impact business decisions. Proficient in Python and advanced analytics tools, with a strong track record of working with large datasets and designing, optimising, and deploying predictive models. Outcome-driven, with expertise in A/B testing to generate insights and guide iterative development. An accomplished leader who builds collaborative, high-performing teams to achieve measurable results. Experienced in leading multidisciplinary research and consultancy projects, aligning technical outputs with strategic objectives, and enabling data-driven transformation across the energy and transport sectors. Exploring computer vision, XR, 3D modelling, and generative AI to investigate how immersive technologies can create novel applications that address real-world challenges.
NESO is an independent, public corporation at the centre of the energy system taking a whole system view to create a world where everyone has access to reliable, clean and affordable energy. I've recently joined the Gas and Hydrogen Data Science Team within Gas and Whole Energy Network Development (GWEND).
The Energy Systems Catapult is an independent research and technology organisation whose mission is to accelerate Net Zero energy innovation. I led a team of data engineers and data scientists, with a strong focus on skills development. - Delivery of research and consultancy projects – led the technical delivery for the data pipelines on the DESNZ-funded ‘Homes for Net Zero’ trial on decarbonisation of homes, which involved processing large quantities of sensor data for 1,200 participants. - Technology launchpad – supported SMEs in developing innovative products. - Data strategy – developed a company-wide data strategy for the Exec, working in partnership with the business leader for Digital and Data; this involved enhancing collaboration between analysts across the business, establishing best practice and reviewing the suitability of the technical platform.
Following the sale of Shell Energy to Octopus Group there were widespread redundancies, including all staff from the IT Division. I decided to take a career break and used this opportunity to study and develop specialisms in computer vision and LLMs. I expanded my knowledge across several tools, techniques and domains through online courses and wider reading. • LLMs and agents – built text classification and summarisation applications using large language model APIs; used LangFlow and LangChain to create a context-aware chatbot with retrieval-augmented generation (RAG). Regularly use generative AI tools as a personal assistant and thought partner for better productivity and faster learning. • Computer vision (OpenCV, Scipy.ndimage, Scikit-image, ...) – undertook online courses in computer vision to gain foundational experience in image processing, classification, segmentation and object detection / tracking. • Deep learning (PyTorch) – built deep neural networks for applications in computer vision and NLP.
Shell Energy Retail Ltd provided home energy to 1.3 million households and broadband to 0.5 million households in the UK. I led the data science function which consisted of a team of five Data Scientists. Sitting in the wider Data & Insights team, we integrated machine learning solutions within business processes to deliver a better customer experience by tailoring multiproduct offerings in home energy, broadband, smart home tech and renewable energy solutions. • Machine learning (Scikit-learn, Scipy.stats) – gained practical knowledge of the advantages and pitfalls of a wide range of techniques, as well as a strong grounding in the theoretical foundations; familiar with both supervised ML for predictive analytics using regression and classification (e.g. linear regression, logistic regression, random forests, SVM, XGBoost, ...) and unsupervised ML (k-means, other clustering, association rules, ...). • Model evaluation and validation – applied best practice and methods to evaluate and improve the performance of ML models (metrics, hyperparameter tuning, cross-validation); used explainability tools to interpret model outputs (SHAP, ...). • Assessing impact – conducted multiple A/B tests to evaluate the performance of prescriptive personalised marketing campaigns; retention campaigns reduced churn, giving substantial financial benefits. • ‘Rocket’ methodology – developed a framework for how machine learning projects were delivered across Shell Energy; this was a bespoke version of CRISP-DM, mapped across three stages of ‘Ignition’, ‘Countdown’ and ‘Lift-off’. The methodology was a key output from a high-profile initiative and was successful in underpinning the ways of working in the multiple cross-functional delivery teams. • Recruitment – built a fantastic team of Data Scientists from external hires, internal career changes, interns and contractors; in the team at Shell Energy average tenure was over five years, which is high for this sector.
• Python programming – applied object-oriented programming and software engineering principles to develop robust Python packages, including comprehensive unit testing and documentation, ensuring scalable and maintainable codebases. • Data analysis and preparation (Pandas, NumPy, SQL) – performed extensive data analysis and preparation, including cleaning, transforming and feature engineering, to ensure high-quality input for machine learning models. • Data visualisation (Seaborn, Matplotlib, Streamlit, Tableau) – created descriptive and diagnostic data visualisations to effectively communicate insights and support data-driven decision-making in a dynamic and interactive manner. • Agile working (Scrum, Kanban) – used agile methodologies and tools to chunk tasks and deliver incremental improvements; championed a continuous improvement mindset in sprint retrospectives to synthesise the best practice identified across the delivery teams. • Customer lifetime value modelling – built the behavioural module of a CLV model with the lead developer and the Finance team; this was part of the Exec's monthly KPI scorecard. • Coaching and mentoring – provided coaching and mentorship to team members, contributing to their professional growth and overall team success. • Working relationships – established good networks with stakeholders, leading to smooth delivery; facilitated constructive dialogue among team members to resolve conflicts. • Dissemination – presented to large audiences at internal seminars, external workshops and conferences to share key findings and raise the profile of the AI work stream. • Upskilling – delivered training sessions on both technical and non-technical subjects. • AI roadmap – maintained a funnel of project proposals working with senior leadership, considering ethical and legal factors; this used an innovative ‘Nine Box’ approach to capture key points for each idea, which were then assessed to prioritise based on feasibility and impact.
First Utility was a home energy and broadband provider, purchased by Shell in 2018. I built several predictive machine learning models and worked with stakeholders to deploy these across the business. • MLOps – utilised selected MLOps best practice for a pragmatic approach to versioning, testing, automation, reproducibility, deployment and monitoring across each of data, ML models and code. • Data science platform (Databricks, AWS, MLFlow, ...) – collaborated with experts on the setup of a shared platform, consisting of Databricks on AWS and other cloud tools; familiar with DevOps techniques to streamline workflows, including CI/CD pipelines, containerisation and orchestration. • Data science architecture – developed a conceptual architecture with seven layers for ML pipelines from source to consumption; this placed emphasis on reusable assets and common libraries in Python. • Manipulation of data pipelines (SQL, APIs, Airflow, ...) – worked in partnership with data warehouse teams on ETL to ingest data (Bronze) and then organise into intermediate processed assets (Silver) and serve to single versions of the truth (Gold); this included data processing at a large scale with assets comprising billions of data points. • Traditional NLP – supervised the delivery of sentiment analysis, text classification and text summarisation projects; the team built pipelines to extract data from 30,000 emails per month, resulting in improved efficiency in operational teams and substantial cost savings. • Forecasting (ARIMA, Prophet, …) – supervised the delivery of time series analysis projects for predicting the required resourcing in the customer service centre.