Mathematician interview questions and practice.
Develops mathematical models and methods to solve problems in science, engineering, finance and technology.
No card for the taster. Full interviews are paid one at a time. Nothing renews.
Last reviewed
This page is still being written: no authored question bank for this competency family. The role is fully supported in the interview itself; only the published question bank is outstanding.
What interviewers for Mathematician actually ask
The question bank for this role is still being written. These are the first three competencies in the model the interview is scored against.
Builds and validates models (pricing, reserving, valuation, capital, forecasting) with sound assumptions, appropriate methods and documented limitations.
Derives assumptions (mortality, lapse, claims frequency and severity, expenses) from data and judgement, monitors actual versus expected, and updates them defensibly.
Applies the relevant regulatory and professional frameworks (e.g. SAM, IFRS 17, ASSA/IFoA standards, or statistical reporting standards) correctly and understands the intent behind them.
What they are really assessing
Interviewers rarely score whether you seemed nice. They score against a model like this one, usually without telling you it exists. Each competency has a weak, adequate and strong shape, and the difference is almost always the level of specific detail you volunteer without being asked.
Actuarial & statistical modelling
Builds and validates models (pricing, reserving, valuation, capital, forecasting) with sound assumptions, appropriate methods and documented limitations.
- Weak
- Runs existing models without understanding the assumptions; cannot explain why a method was chosen, what the model is sensitive to, or a case where the model was wrong.
- Adequate
- Explains the method and key assumptions of a model they built or maintained, and has validated it against experience, but sensitivity and limitations are described only in general terms.
- Strong
- Walks through a model with the assumption choices and their evidence, the sensitivity and back-testing done, a limitation that mattered in practice, and how they communicated model risk to users.
Assumption setting & experience analysis
Derives assumptions (mortality, lapse, claims frequency and severity, expenses) from data and judgement, monitors actual versus expected, and updates them defensibly.
- Weak
- Takes assumptions as given; cannot describe an experience investigation, credibility weighting or a case where actual experience diverged from expected.
- Adequate
- Has run experience analyses and proposed assumption changes with credibility considerations, but the judgement overlays are not well justified and monitoring is infrequent.
- Strong
- Describes an actual-versus-expected finding that changed an assumption, the credibility and trend judgement applied, how it was challenged in review, and the financial effect of the change.
Regulatory & professional standards
Applies the relevant regulatory and professional frameworks (e.g. SAM, IFRS 17, ASSA/IFoA standards, or statistical reporting standards) correctly and understands the intent behind them.
- Weak
- Names the regulations but cannot explain their requirements for a piece of work they did, or the difference between a regulatory and an economic view.
- Adequate
- Applies the relevant standards to their work and prepares required reports, but cannot discuss where the standards involve judgement or how they handled an interpretation question.
- Strong
- Describes a judgement call under a standard (e.g. contract boundary, risk margin, discount rate), how they justified it, how it was reviewed, and a case of pushing back on a non-compliant request.
Data handling & modelling tools
Prepares and validates data rigorously and uses tools (Excel, R, Python, SQL, actuarial software) in a controlled, reproducible way.
- Weak
- Work is manual spreadsheets with no version control or checks; cannot describe a data error that affected results or how it was found.
- Adequate
- Uses scripts and controlled spreadsheets with reconciliation checks, and has caught data errors, but reproducibility and peer review are inconsistent.
- Strong
- Describes a controlled modelling environment with version control, automated checks and peer review, a data error caught by these controls and its potential impact, and how they improved the process.
Communicating technical results to non-actuaries
Explains model results, uncertainty and implications to executives, boards, underwriters or regulators in terms that support decisions.
- Weak
- Presents tables of results without interpretation; cannot explain a reserve movement or price change to a non-technical audience; avoids stating uncertainty.
- Adequate
- Explains results with drivers and ranges and has presented to management, but tends to lead with method and struggles when challenged on judgement.
- Strong
- Gives a case of explaining a difficult result (reserve strengthening, price increase, capital shortfall) to executives or a board, how they framed uncertainty and options, and the decision taken.
Professional judgement & integrity
Exercises independent professional judgement, resists pressure to adjust results, and documents and escalates concerns appropriately.
- Weak
- Cannot describe a situation involving pressure on results or an ethical dilemma; defers to whoever is senior.
- Adequate
- Has held a position under pressure with support from a senior actuary, but cannot articulate the professional obligations involved.
- Strong
- Describes a specific case of resisting pressure to change a result or assumption, the professional standards invoked, how they escalated or documented it, and the outcome.
Commercial & business understanding
Understands how models connect to product, pricing, profitability and risk appetite, and contributes to business decisions rather than only reporting numbers.
- Weak
- Treats the work as calculation; cannot explain how a pricing change affects sales, lapses or profitability, or what the business did with their results.
- Adequate
- Understands the product and market context and has contributed to a business decision, but the analysis of second-order effects is limited.
- Strong
- Describes a business decision they influenced (product design, pricing strategy, reinsurance) with the commercial trade-offs, the second-order effects considered, and the measured outcome.
Reading the questions is the easy half. Try answering three of them out loud, to someone who follows up.
Try 5 minutes freeWhat your 30 minutes covers
The same shape as a real first-round interview, pitched at mid-level Mathematician and scored throughout.
Warm-up, then Motivation & fit
Build rapport, settle nerves, and get a short walk-through of your background. Why this role, why this employer, and what you are actually looking for.
Your experience
Two or three real situations from your CV in depth: context, what you did, what happened, what you would change.
Pitched at mid-level scope: owns a process or product area (e.g. reserving for a line, pricing a product) end to end, including assumptions and reporting; often newly qualified.
Role-specific questions
The core competencies and domain knowledge for the role, with follow-ups on anything vague.
Drawn from this role's domain: setting a mortality, lapse or claims assumption from experience data, actual versus expected analysis and when to change an assumption and reserving methods: chain ladder, Bornhuetter-Ferguson and their limitations, and the rest of the competency model.
Your questions, then Wrap-up
Your questions for the interviewer, and yes, they are assessed. Next steps and a clean finish.
What changes with seniority
The questions barely change between levels. What changes is the answer they will accept.
| Junior | Mid | Senior | |
|---|---|---|---|
| Scope of ownership | Owns defined pieces of valuation, pricing or analysis work with review; maintains models and runs regular processes. | Owns a process or product area (e.g. reserving for a line, pricing a product) end to end, including assumptions and reporting; often newly qualified. | Owns a function's technical output (valuation, pricing, capital) and its methodology; accountable for results signed off to management or regulators. |
| Tolerance for ambiguity | Handles routine work independently and escalates unexpected results promptly. | Sets assumptions with judgement, handles novel questions, and knows when to escalate. | Defines methodology where standards leave judgement; handles regulatory change and novel products. |
| People leadership | No formal leadership. | Supervises students and reviews their work. | Leads a team of actuaries and students, reviews and signs off work, develops people. |
| Who they deal with | Own team, senior actuaries, occasionally finance or underwriting. | Finance, underwriting, product, risk, auditors. | Executives, board committees, regulators, auditors, reinsurers. |
What your report would say
Every competency above scored from your own answers, the sentence that cost you quoted back, and your weakest answers rewritten the way a strong Mathematician would have said them.
- Describes a judgement call under a standard (e.g. contract boundary, risk margin, discount rate), how they justified it, how it was reviewed, and a case of pushing back on a non-compliant request.
The format, not a result. Scores on your report come from what you actually said.
Is the AI interviewer realistic? See a full sample report