Anton Robb

Promoted to supervisor after three months in a busy live music venue. Trained and supervised bar teams in a high-pressure environment, ensuring efficient service and compliance with cash-handling procedures. Led teams through large events and late-night shifts, balancing customer service with speed and accuracy. Recognised for reliability and initiative, and won prizes for sales performance, while working alongside full-time study and employment.

Wrote T-SQL daily to extract and transform financial data from multiple systems, and built more than 280 client-facing dashboards in QlikView. Used advanced Excel (pivot tables, lookups, conditional logic) to clean and reconcile data. Resolved data quality issues across hundreds of dashboards through systematic validation and clear explanations to non-specialist audit stakeholders, from trainees to senior managers. Met concurrent reporting deadlines across multiple audit cycles.

Performed accuracy checks on financial statements, aligning records with prior-year benchmarks and flagging discrepancies for resolution. Cross-verified company data against Companies House records so that audit reports met legal standards. Progressed from preparing work to reviewing colleagues’ audit work after two months, checking it for accuracy. Used Excel and Microsoft Teams to support audit reviews and keep communication clear across the team.

Joined a busy live music venue as bar staff and was promoted to supervisor after three months. Served customers quickly and accurately at large events, handled cash and card payments to venue procedures and worked varied shifts, including late-night events. Took on additional delivery shifts to support stock replenishment and venue upkeep. Built strong working relationships with the team and was recognised for reliability and initiative.

Awarded Distinction. Modules covered statistical methods in R, machine learning and data mining, big data computing (Hadoop, PySpark), Bayesian modelling and exploratory data analysis. My dissertation, on Bayesian inference for a stochastic movement model in R earned 82%, the highest mark in my cohort, and is being prepared for journal submission. Projects included gradient boosting, time series forecasting, Monte Carlo simulation and a batch and real-time sentiment pipeline.