Muhammad Bavla

First Class BSc (Hons) Actuarial Science & Mathematics graduate from the University of Manchester (82% average) with 5 IFoA exemptions (CS1, CS2, CM1, CB1, CB2). Strong technical background in statistical modelling, stochastic processes, survival analysis and financial risk modelling, with practical experience using Python, R and Excel to analyse uncertainty and support data-driven decision making.

Seeking Graduate

Actuarial opportunities within General Insurance Pricing, Reserving, Capital Modelling or Life & Reinsurance.

Intern at Government Statistical Service (2024-06 – 2024-08)

• Led an independent data analysis project of a dataset exceeding 100,000 observations using R, developing forecasting models to analyse fiscal outcomes under uncertainty.
• Built models incorporating macroeconomic variable and distributional assumptions, improving projection accuracy.
• Performed scenario analysis and stress testing to assess extreme outcomes and downside risk, identifying key drivers of variation and model uncertainty.
• Analysed volatility and model uncertainty, identifying key drivers of variation in projections and interpreting statistical results to support evidence-based decision making.
• Collaborated with stakeholders including KPMG and IBM to align modelling assumptions, analytical requirements and data outputs.
• Delivered reports translating complex statistical outputs into clear insights for decision-making under uncertainty

BSc (Hons) in Actuarial Science & Mathematics – The University of Manchester (2023-09 – 2026-07)

A-Levels – Queen Elizabeth's Grammar School (2021-09 – 2023-06)