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• Led Phase 1 of the Future Risk Modelling Programme, designing a novel methodology to quantify train accident costs (derailments, collisions) by integrating SQL-extracted data with open-source datasets. • Developed a dynamic Excel cost model parameterised by speed/train type, validated against 20+ years of insurance data. • Authored the Phase 1 report, achieving Gate 2 approval and securing £80K in funding for phase 2.

• Conducted a deep-dive analysis into key drivers of increased incidents occurring from objects left on the line by trackworkers during engineering works using SQL queries. • Transformed the data, developed interactive dashboards in Power BI, and compiled a detailed report with targeted recommendations to address root causes, which contributed to a 20% average reduction in reported track obstructions within 3 months. • Collaborated within a 3-person team to migrate over 20 Train Operating Companies to Power BI from an existing safety business intelligence solution. • Developed user guidance notes, virtual training sessions to end-users, and actively participated in Quality Assurance (QA) testing with Data Engineers to ensure a seamless transition from existing reporting solutions. • Designed self-serve dashboards tailored to stakeholder needs where required, enabling users to explore key metrics independently and improving reporting efficiency. • Significantly enhanced stakeholder adoption and proficiency with Power BI, contributing to minimal disruption in reporting workflows and earning a Net Promoter Score (NPS) of 50. • Automated time-series data analysis presented to railway industry groups by transitioning from manual Microsoft Excel workflows to Python, reducing preparation time, and improving accuracy.

• Managed end-to-end production of Common Safety Indicators (CSIs) for GB rail operators, delivering product 4 weeks early via SQL and Excel. • Created a CSI production guide to standardise processes, ensuring future efficiency.