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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.
No degree needed for many routes
AI impact: high££££ payDirect entry route
62
AI impact
how much AI is reshaping it
Robin · your guide
Curious about being a quantitative analyst? Here's the honest picture - what you'd really do, what you'd earn, and every way in. No need to decide anything yet.
What you'd actually do
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.
1Develop and implement sophisticated mathematical models to assess financial risk.
2Analyze large datasets to identify trends and patterns that inform investment decisions.
3Collaborate with traders and portfolio managers to optimize investment strategies.
4Present findings and recommendations to stakeholders through detailed reports and presentations.
5Continuously monitor market conditions and adjust models to reflect changing dynamics.
6Utilize programming languages such as Python or R to automate data analysis processes.
7Conduct back-testing of trading strategies to validate their effectiveness.
8Stay updated with the latest financial regulations and economic indicators impacting the market.
Career progression & pay
01
Getting in
Junior Quantitative Analyst
£30,000 - £40,000
BSc in Mathematics, Statistics, or related field
In this entry-level role, you will assist in data collection and preliminary analysis, gaining hands-on experience with quantitative methods and tools.
02
Building up
Mid-level Quantitative Analyst
£50,000 - £65,000
3-5 years experience + proficiency in programming languages
At this stage, you will take on more complex projects, developing models independently and collaborating closely with senior analysts and traders.
03
At the top
Senior Quantitative Analyst
£75,000+
10+ years, chartered status with a professional body such as the CFA
In a senior role, you will lead projects, mentor junior analysts, and play a key role in strategic decision-making within the organisation.
Degrees that lead here via Finance & Accounting
Degree options are mapped from subjects - explore the buckets to find related courses.
Apprenticeships that lead here
Who hires - top UK employers
Goldman Sachs
A leading global investment banking firm, known for its innovative approach to quantitative finance.
Barclays
One of the UK's largest banks, offering diverse opportunities in quantitative analysis across various financial sectors.
J.P. Morgan
A top-tier financial services firm with a strong emphasis on data-driven decision-making and quantitative research.
HSBC
A global bank with a commitment to innovation in finance, providing a dynamic environment for quantitative analysts.
Morgan Stanley
A leading global financial services firm that values quantitative analysis in its investment strategies.
AI & the future of this job
Quantitative analysis sits in a genuinely contested space: AI tools are already accelerating model development, data processing, and backtesting at speed no human team can match manually. The entry-level grunt work of cleaning data, running regressions, and drafting initial model frameworks is increasingly handled by AI agents, which is compressing the junior pipeline significantly. However, the interpretive layer, knowing which model assumptions are defensible in a live market, reading counterparty behaviour, and making calls under genuine uncertainty, still requires experienced human judgement. The field is not disappearing, but it is restructuring fast, and the path in is narrowing.
Within 5 Years
Junior roles contracting sharply
By 2031, AI coding and analysis agents will handle a substantial portion of what graduate quants currently spend their first two years doing: data wrangling, factor analysis, model documentation, and performance reporting. Firms will hire fewer entry-level analysts and expect those they do hire to operate more like mid-level contributors from day one. The roles that remain will be better paid but harder to break into without demonstrable applied skills beyond the degree itself. Building a live portfolio of model work before graduating will shift from impressive to essentially mandatory.
Within 10 Years
Redefined, senior-skewed profession
By 2036, the quantitative analyst role will likely look closer to what a senior quant does today: model governance, regime identification, risk oversight, and translating ambiguous business problems into tractable quantitative frameworks. AI will generate candidate models rapidly, but human analysts will be accountable for validating assumptions, stress-testing edge cases, and defending choices to regulators and boards. The workforce size may be 30 to 40 percent smaller than today's, but the individuals in it will carry significantly more responsibility and command accordingly. Specialisations in areas like AI model risk, alternative data, and systematic macro are likely to be the growth pockets.
Within 20 Years
Human-AI oversight role
The twenty-year horizon is genuinely uncertain, but the most plausible outcome is a profession where very few people are called quantitative analysts by title, yet the underlying skill set is embedded across finance, regulation, and risk management more broadly. Those who built careers on deep mathematical intuition combined with strong communication and governance skills will transition into roles overseeing AI-driven financial systems rather than being replaced by them. The danger is for anyone who treated the role as primarily technical execution rather than applied judgement. Ultimately, the quants who thrive will be those who stayed curious about the models rather than just proficient at running them.
How to stay ahead
Specialise in model risk and AI governance
Financial regulators globally, including the FCA in the UK, are increasing scrutiny of AI-driven trading and lending models. A quant who understands both the mathematics and the governance frameworks around model validation is exceptionally valuable and hard to automate. Seeking out roles or modules that cover model risk management directly positions you in a growth area rather than a shrinking one.
Build skills in alternative and unstructured data
Satellite imagery, shipping data, social sentiment, and supply chain signals are increasingly central to systematic investment strategies, yet extracting usable signals from these sources requires genuine ingenuity that AI tools alone do not provide reliably. Developing practical experience processing and interpreting non-traditional datasets gives you an edge that pure statistical modelling skills no longer offer. Look for university projects or competitions like those run by WorldQuant that involve real alternative data sets.
Develop stakeholder communication as a core competency
AI can produce analysis, but it cannot yet defend it credibly in a room with a sceptical risk committee or a nervous institutional client. Quants who can translate probabilistic thinking into plain English, run effective presentations, and handle challenge under pressure are disproportionately valuable precisely because so few technical people invest in this deliberately. Treat public speaking, structured writing, and stakeholder management as professional skills equal in importance to your technical toolkit.
Get industry exposure before graduation
The gap between what university teaches and what firms actually use has widened sharply in quantitative finance, and a strong academic record alone is insufficient differentiation in 2026. Internships at hedge funds, prop trading firms, or quantitative teams within banks give you exposure to real production environments, live data, and the operational realities of model deployment. Even unpaid or lightly paid analytical project work with a fintech, if the technical content is genuine, signals readiness in a way that transcripts cannot.
How to get in - your routes
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