Promoted to Senior Transfer Pricing Analyst, where I spearheaded the design of tax segmentation and shared services business frameworks. These initiatives streamlined global operations by enhancing financial modeling accuracy, scalability, and audit transparency across corporate tax functions.

Key Achievements

• Designed comprehensive tax segmentation frameworks that allocate profits and costs across entities using rule-based logic, reducing manual reconciliation efforts by 80% and improving operational efficiency.
• Developed shared services allocation models with policy-aligned weighting mechanisms, enabling precise cost distribution and compliance reporting in minutes rather than weeks (95% time savings).
• Integrated legacy transfer pricing tools (cost-plus, global services) into a unified segmented P&L system via Python modules and Power BI, providing scalable analytics for service and manufacturing pipelines with 100% entity onboarding automation.
• Extended financial modeling capabilities to handle complex entity onboarding and economic activity breakdowns, minimizing errors by 99% and supporting strategic tax planning.

As Transfer Pricing Analyst, I automated critical financial and compliance processes through advanced modeling and data tools, replacing error-prone manual workflows with scalable solutions that accelerated month-end closings and ensured robust tax adherence.

Key Achievements

• Built a cost-of-shared-services allocation model that automates cost weighting and reporting, slashing two weeks of manual work to minutes (95%+ time reduction) while eliminating human errors.
• Engineered a fully integrated month-end closing automation, saving ~£40,000 annually (one FTE equivalent), compressing multi-team processes from hours to 5 minutes (99% time savings), and enabling seamless scalability.
• Created Power BI and VBA-driven cost-plus and intercompany invoice compliance checkers for 10,000+ monthly invoices, achieving 100% policy alignment coverage and reducing compliance risks.
• Automated quarterly pricing cycles with Python/Go processors for 10,000+ prices, including SAP formatting, mark-up adjustments, and load preparation – cutting processing time by 90% and error rates to near zero.
• Delivered dynamic P&L dashboards and intercompany transaction classifiers using Power BI and SAP BW data, streamlining global tax audits by 70% and enhancing operational reporting accuracy.

As Commercial Finance Analyst at Radius, I developed advanced customer cohort segmentation and automated reporting pipelines using SQL, Python, and Tableau, delivering ROI tracking, churn analytics, and ML-driven revenue forecasts to optimize commercial performance across 11 countries.

Key Achievements

• Pioneered rolling/fixed cohort models (front-book, second-year, back-book) via optimized Redshift SQL with CTEs (common table expressions) and regex, enabling precise lifecycle analysis and cutting churn prediction cycles by 80% for retention strategies.
• Created production Git repository with Python testing suite and SQL datasets to track marketing ROI from lead to onboarding, implemented by BI team – improving channel attribution accuracy by 40% and supporting high-stakes investment decisions.
• Automated monthly executive reports for sales/marketing across 11 countries using Python-Redshift-Excel API bridge, slashing production time by 85% while ensuring 100% data veracity for stakeholder forecasting.
• Invented “customer start window” metric and built Tableau dashboards segmenting large/small customers, reducing analysis time from weeks to hours (90% efficiency gain) and enhancing revenue modeling precision.
• Delivered Bayesian time series and polynomial regression models for core product revenue forecasts (90% accuracy), mastering data’s four Vs to drive commercial planning and high-finance caliber insights.