Overview
Actuarial graduate resumes all look the same. They list "CT1 passed" and "strong numeracy skills" and hope that an employer will fill in the blanks. The problem is that every maths graduate applying for these roles has strong numeracy skills. That is the baseline, not a differentiator.
This resume belongs to Priya, a Mathematics BSc graduate from the University of Warwick who interned at Aviva's pricing team and has passed two IFoA exams (CM1 and CS1). What makes her resume work is that she translates her mathematical skills into business language. Instead of "performed statistical analysis," she writes "built a GLM pricing model that reduced quote to bind leakage by 3.2% across the motor book."
That is the difference between sounding like a student and sounding like someone who can do the job.
Actuarial exams belong in a prominent position
Hiring managers for actuarial roles scan for exam progress before anything else. If you have passed one or more IFoA or IFOA exams, list them in your certifications section with the date passed. If you are sitting an exam soon, include it with "Sitting [month] 2026" so they know you are progressing through the qualification.
Priya lists CM1 and CS1 as passed, with CB1 scheduled. That tells the employer she is on track and self motivated about the qualification pathway.
Internship experience with numbers
For actuarial and data analyst roles, your internship is where you prove you can work with real data at scale. Priya's Aviva internship shows she worked with a motor insurance book, built pricing models in R, and quantified her impact in pounds.
"Cleaned and validated 180,000 policyholder records" shows she can handle messy real world data. "Reduced quote to bind leakage by 3.2%" shows she understands the business outcome. These details matter far more than listing R and Python in your skills section.
University projects as proof of analytical thinking
Your dissertation or final year project is your chance to demonstrate independent quantitative research. Priya's project on Bayesian network models for credit risk shows advanced methodology, a real dataset (5 years of Lending Club data, 2.3 million records), and a clear finding (12% improvement in default prediction accuracy over logistic regression).
A hiring manager reading this knows she can scope a problem, choose appropriate methods, work with large datasets, and evaluate results. That is exactly what a junior actuary does on day one.
Software skills: be specific about what you built
"R" on its own tells the reader nothing. "R (tidyverse, glmnet, ggplot2)" tells them you do data wrangling, modelling, and visualisation. Priya also lists Python with pandas and scikit-learn, SQL, and Excel VBA. Each tool is paired with context so the reader knows what kind of work she used it for.
