Statistician CV Examples & Writing Guide

This sample works in clinical research at a contract research organisation. Employer names are left out. Notice that the study designs, phases and methods are named, because that is how statisticians are matched to open roles.

Statistician roles vary enormously between pharma, government, insurance, tech and market research. All of them screen on the same four things.

• Designs you have worked with. Randomised trials, observational cohorts, complex surveys, online experiments, quality control. This decides whether your experience transfers.
• Methods you can defend. Anyone can list model names. Say which ones you chose and why, and name the sensitivity analyses you ran when the assumptions were shaky.
• Software you actually write in. SAS in pharma and government, R almost everywhere, Python where the work touches engineering. Say which one you write daily and which you can read.
• Whether the analysis changed a decision. A statistician who can point to a programme stopped, a protocol amended or a model that replaced a rules-based process is far more hireable than one who only produced outputs.

“I look for one analysis explained in plain language with the reason for the method choice. If a candidate can tell me why they used a mixed model rather than a t-test on change scores, and what they did about dropout, the technical screen is basically over.”

Three or four lines: sector, designs, methods, software, and one thing your analysis changed.

The most common failing on a statistician CV is a list of techniques with no study, no size and no consequence.

Group by design, method and tooling. Only list what you can be questioned on for half an hour.

• Design: randomised trials including adaptive and cluster designs, observational cohorts, case-control, complex survey sampling, online experimentation, quality control and acceptance sampling.
• Inference: generalised linear models, mixed and hierarchical models, survival analysis, longitudinal methods, Bayesian methods if you use them, multiple comparison control, bootstrap and resampling.
• Causal work: randomisation, propensity scores, instrumental variables, difference-in-differences, regression discontinuity, and the assumptions each one needs.
• Data handling: missing data methods, measurement error, weighting and post-stratification, disclosure control for published statistics, data quality checks.
• Software: SAS including macro programming, R and the tidyverse, Python with statsmodels or scikit-learn, SQL, Stata if relevant, plus version control and reproducible reporting.
• Domain and communication: regulatory frameworks in your industry, protocol and analysis plan writing, presenting results to non-statisticians, peer review and manuscript writing.

Statisticians are one of the few groups who genuinely need two versions of the CV. Sending the wrong one is a common and avoidable mistake.

Many statistician jobs ask for a master's degree, so the entry-level CV is often written by someone finishing an MSc. The job is to show applied work, not modules.

1. 1Lead with your MSc dissertation or final-year project, described like a job: the design, the sample size, the method and the finding.
2. 2List research assistant posts, consultancy unit work, placements or internships with the same structure. A university statistical consultancy unit is real client experience and hiring managers value it.
3. 3Name the software you write in daily and be honest about depth. Add a SAS Base or Advanced Programmer certification if you are targeting pharma or government.
4. 4Show reproducibility habits: version control, R Markdown or Quarto, documented code. Employers ask about this more than new graduates expect.
5. 5Include a short skills block covering design, methods and software, so a recruiter without a statistics background can match keywords.
6. 6Keep coursework to one line of the most relevant advanced modules, or cut it entirely once you have any applied project to show.