Writing a strong Mathematician CV
Hiring managers and academic panels look for a Mathematician CV that identifies your field quickly, such as algebra, topology, numerical analysis, statistics, optimisation, cryptography, or mathematical biology. Include research outputs, preprints, conference talks, teaching responsibilities, and any collaboration with engineering, finance, defence, software, or data science teams. For industry roles, show practical tools such as Python, R, MATLAB, Mathematica, LaTeX, Git, SQL, or C++, plus deliverables like simulation models, forecasting methods, optimisation algorithms, statistical reports, or peer-reviewed papers. If relevant, list grants, fellowships, postdoctoral appointments, supervised theses, and high-performance computing experience.
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Three things that matter most
- Separate pure and applied work clearly: list theorem-proving, publications, and seminar talks apart from modelling, simulation, or algorithm development so the reader can understand your mathematical profile quickly.
- Include links to arXiv papers, GitHub repositories, ORCID, Google Scholar, or a personal research page, especially if your work involves computational experiments or reproducible notebooks.
- For industry applications, translate your research into business or technical deliverables, such as risk models, routing algorithms, pricing methods, anomaly detection, or numerical solvers used by non-academic teams.
Skills hiring managers look for
Frequently asked
How do I write a Mathematician CV with no industry experience?
Use academic and project evidence to show applied capability. Include dissertation topics, research assistant work, coding projects, modelling assignments, conference posters, and any use of Python, R, MATLAB, or LaTeX. If applying outside academia, add a short project section that explains the problem, method, data or assumptions, and final output.
Should a Mathematician CV include publications and preprints?
Yes, if you are applying for academic, research, or technical roles where mathematical depth matters. Separate peer-reviewed papers, preprints, invited talks, and conference presentations so reviewers can assess the status of each item. Include DOI, arXiv, or journal details where available.
What is the best CV format for a Mathematician moving into data science?
Use a hybrid format that keeps your mathematical training visible while foregrounding applied tools and projects. Place Python, R, SQL, machine learning, optimisation, and statistical modelling near the top, then describe research projects in terms of datasets, algorithms, validation methods, and decisions supported. Keep long publication lists in a separate section or appendix if they are not central to the role.
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