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Quantitative Analyst

Quantitative analysts use maths and computer coding to understand money markets and help investors make better decisions. They spot patterns in numbers and build tools to predict how markets might move.

Quantitative Analyst

60

Finance & Accounting

AI IMPACT
AI CAN DO60%moving to95%
THE DOOR, NOWC
THE DOOR, 20 YRSE
STARTING PAY£30,000 - £40,000
NO MOATNo physical, legal or personal barrier protects this work from software.
This work is being absorbed by AI
AI already does much of this work and nothing structurally requires a human: no licence, no physical task, no relationship at the core. Expect fewer people in higher-judgment roles: enter as the person directing the tools, not competing with them.

The role

As a quantitative analyst, you turn piles of financial data into clear answers that help traders and investors. You use maths, statistics and coding (usually Python or R) to find patterns that might make money or spot risks. It's like solving puzzles with numbers - you need to be detail-focused and good at maths, but also be able to explain what you've found so other people understand.

Most days you'll be at your computer, pulling data, testing ideas and building models. You work with traders to understand what they need, then create tools that help them. You might look at how stocks have moved in the past to predict the future, or test whether a new trading idea would have made money. The job needs you to be careful - a small mistake in a model can cost real money - and to keep learning as markets change.

Daily responsibilities

  • Develop and implement sophisticated mathematical models to assess financial risk.
  • Analyze large datasets to identify trends and patterns that inform investment decisions.
  • Collaborate with traders and portfolio managers to optimize investment strategies.
  • Present findings and recommendations to stakeholders through detailed reports and presentations.
  • Continuously monitor market conditions and adjust models to reflect changing dynamics.
  • Utilize programming languages such as Python or R to automate data analysis processes.
  • Conduct back-testing of trading strategies to validate their effectiveness.
  • Stay updated with the latest financial regulations and economic indicators impacting the market.

Does a degree help here?

A UK degree, particularly in Mathematical Sciences or a related field, provides a robust foundation in analytical thinking and problem-solving. UK universities are renowned for their rigorous curricula and strong industry connections, giving graduates an edge in securing competitive roles in finance and analytics.

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AI EXPOSURE = the share of a job's day-to-day tasks AI can already do today, from the same exposure engine used across this site (Anthropic labour market research, 2026, observed real-world AI usage by occupation). Higher means more exposed. It is a measurement of now, not a prediction. THE DOOR = how hard the job is to get into, grade A (easy) to E (extremely hard), from each career's published forecast (OpenAI, "The AI Jobs Transition Framework", Richmond 2026, CC BY 4.0). A card marked MOAT NOT YET CLASSIFIED has a real exposure score but no entry yet in our moat register, so we make no claim about what structurally protects it. Scorecard grades and verdicts are Careermash editorial judgment: we show forecasts as forecasts and own our conclusions. Salary and pathway figures are each career's own published profile. Careermash is a service provided by What School Ltd.

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