top of page

Resume

Work Experience

McLaren Automotive - BI/Data Scientist & Project Coordinator

2024 - Present

Tesla Motors - Senior Support Analyst/Advisor                     

2024 - 2025

London Overground Train Services (Carlisle Support Services) - Customer Service Team Lead

2020 - 2022

Independent Consultant - Data Scientist & BI Developer

2017 - Date

United Bank for Africa – Business Insight Analyst

2013 - 2017

  • Designed and deployed machine learning models in Python/Databricks for predictive maintenance and aftersales demand forecasting, boosting forecast accuracy by 35% and reducing emergency repairs by 40%.

  • Built production-grade Azure Data Factory pipelines to ingest telematics, warranty, and customer feedback data into a centralised, governed data lake, halving manual ETL effort.

  • Developed interactive Power BI dashboards with scenario-analysis capabilities for operational decision-making across service centres.

  • Implemented MLflow tracking for model version control and automated deployment through GitHub Actions and Docker-based containers in Azure.

  • Automated root-cause analysis by parsing vehicle logs with Python, templated dashboards for support KPIs, and statistical summaries—reducing average ticket resolution time by 60%

  • Proactive Telematics Monitoring & Alerting. Utilized remote diagnostics and telematics data to detect anomalies in battery health, thermal management, and firmware behaviour, preventing over 300 potential on-road failures through timely customer interventions and software patching

  • Cross-Functional Collaboration with R&D & Aftersales. Acted as a key liaison between frontline support and engineering, translating recurring field issues into actionable feedback—contributing to two major firmware releases and multiple UX enhancements based on customer pain points

  • Team Leadership & Mentorship. Directed and mentored a team of support advisors, developing and delivering training programs on consumer finance application processes across both B2B and B2C segments, significantly enhancing overall sales performance and customer engagement.

  • Delivered interactive Power BI reports that visualized customer journey metrics and financial-application KPIs, driving a culture of evidence-based service improvements.

  • Process Optimization. Utilized data-driven insights to streamline customer service operations, which resulted in improved response times and elevated satisfaction metrics.

  • Stakeholder Communication. Collaborated extensively with cross-departmental teams to ensure that service enhancements and BI updates aligned with strategic business goals.

  • Designed and deployed machine learning models (Python, scikit-learn, pandas) to forecast sales, optimise inventory, and identify high-value customer segments for SME clients—resulting in up to 20% improvement in sales conversion rates.

  • Built SQL-based data pipelines to consolidate transactional, CRM, and web analytics data, reducing manual data preparation time by 70%.

  • Delivered automated, interactive dashboards in Power BI and Tableau, enabling real-time performance monitoring and reducing reporting lag from days to minutes.

  • Applied predictive analytics to marketing datasets, creating ROI attribution models that improved campaign efficiency by 15% and informed targeted ad spend strategies.

  • Implemented data governance best practices and documentation standards, ensuring scalability and compliance with GDPR for all client analytics environments.

  • Conducted A/B testing and statistical analysis to validate product pricing and promotional strategies, increasing average order value by 12%.

  • Trained client teams on SQL, Python for Data Science, and BI visualisation tools, improving in-house analytics capabilities and reducing dependency on external contractors.

  • Customer Segmentation & Campaign Optimization: Leveraged SQL and Excel to analyze customer data, segmenting high-value demographics and refining marketing strategies. Automated reporting dashboards in Excel reduced manual analysis time by 30%, directly contributing to a 10% increase in marketing campaign ROI.

  • Scalable Reporting Systems: Designed SQL-driven data models and Excel-based reporting frameworks to streamline transactional data analysis. These tools enabled real-time insights into customer acquisition trends, reducing operational costs by 5% while improving decision-making accuracy.

  • Cross-Department Collaboration: Partnered with marketing and finance teams to translate raw data into actionable insights using advanced Excel functions (e.g., Power Query, PivotTables) aligning analytics with business objectives.

Education

2024

Loughborough University | Master’s Degree

Artificial intelligence & data analytics

2025

Udacity | Machine learning 

Machine Learning with Pytorch

machine Learning with TensorFlow

Deep Learning

2025

Udacity | AWS NanoDegree

AWS Machine Learning Nanodegree

2024

Microsoft | Azure AI Fundamentals

Azure AI FUndamentals

Skills
& Expertise

  • Machine Learning & Optimisation: Supervised/unsupervised learning, time-series forecasting, mixed-integer programming, heuristics, anomaly detection, clustering, regression.

  • Programming & Data Engineering: Pytorch, TensorFlow, Python, SQL, Azure Data Factory, AWS (S3, Lambda, Redshift), Databricks, PostgreSQL, Docker, Git, MLflow, DVC.

  • Model Deployment & Orchestration: CI/CD (GitHub Actions), orchestration (Airflow), model testing (unit/integration/E2E).

  • Analytics & Visualisation: Power BI, Seaborn, Matplotlib, Tableau.

  • Business Impact: Agile delivery, stakeholder engagement, operational efficiency, cost optimisation.

bottom of page