Where mathematics meets markets.
Quants use advanced mathematics, statistics, and computer science to build models that drive trading decisions, price financial instruments, or manage risk. You are the unseen infrastructure behind billions of pounds in systematic strategies, and among the highest-paid people in any industry.
Year 1
£90K-£170K
Base: £70K-£110K
Bonus: £20K-£60K
Year 3
£170K-£320K
Base: £110K-£170K
Bonus: £60K-£150K
Senior
£320K-£700K+
Base: £170K-£300K
Bonus: £150K-£400K
London figures, carried across as a starting point. Correct these in the admin editor as needed.
Weekly Hours
50-70 hrs/week
Lifestyle
Deep focus · Research-driven · High autonomy
Quant roles span both sides
Quants exist at banks doing pricing, structuring and risk (sell-side), at prop trading firms doing HFT and systematic trading (proprietary), and at hedge funds doing alpha research (buy-side). The skill set transfers, but the culture and output differ enormously. Prop firms are the most meritocratic and highest-paying early in your career.
Quant Researcher (Trading Signals)
Build and test statistical models to find exploitable patterns in market data. You develop 'alphas', signals that predict future price movements. The job is like academic research but the feedback loop is market P&L. Jane Street, Two Sigma, D.E. Shaw.
Quant Developer / Strat
Implement trading strategies and infrastructure. Code is the craft, Python, C++, and increasingly Rust. Sit between research and engineering. Often called 'Strats' at banks. Goldman Sachs Strats are legendary.
Derivatives Pricing Quant
Build mathematical models to price complex derivatives, options, structured products, exotic instruments. Heavy on stochastic calculus, PDEs, and Monte Carlo simulation. Work at investment banks and hedge funds.
Risk Quant
Build models to measure and manage financial risk, VaR, stress testing, scenario analysis. More stable and less high-octane than trading quants. Work at banks, regulators, and large asset managers.
High-Frequency Trading (HFT) Quant
Trade at microsecond timescales. Models are less about long-term alpha and more about market microstructure, how order books work, latency arbitrage, market-making. Optiver, Virtu, Jump Trading, DRW. Requires ultra-low-latency C++ skills.
Route In
Internship (penultimate year) → online tests + brainteasers → graduate quant/researcher scheme (PhD route for research)
Technical Topics Tested
Sample Interview Questions
You roll two fair dice. What is the probability that the sum is 7?
Count favourable outcomes: (1,6), (2,5), (3,4), (4,3), (5,2), (6,1) = 6 outcomes. Total outcomes = 36. P(sum=7) = 6/36 = 1/6. In interviews, show your working explicitly, quant interviewers want to see your enumeration process, not just the answer. Follow up: 'What's the expected value of the sum?' → E[sum] = E[die1] + E[die2] = 3.5 + 3.5 = 7.
What is Ito's Lemma and why does it matter?
Ito's Lemma is the chain rule of stochastic calculus (it tells you how a function of a stochastic process changes over time. If dS = μS dt + σS dW (GBM), and f = f(S,t), then df = (∂f/∂t + μS ∂f/∂S + ½σ²S² ∂²f/∂S²)dt + σS ∂f/∂S dW. It matters because it's the foundation of Black-Scholes) you use it to derive the option pricing PDE. The key insight is the extra ½σ²S² ∂²f/∂S² term that arises from the quadratic variation of Brownian motion (unlike regular calculus where (dW)² → 0, in stochastic calculus (dW)² = dt).
A market maker quotes a bid-ask spread. When does it widen?
Spreads widen when: 1) Inventory risk increases (maker is stuck with a position they can't hedge. 2) Adverse selection risk increases) maker worries they're trading against informed traders who know more. 3) Volatility increases (uncertainty in fair value increases. 4) Liquidity decreases) fewer participants to offset risk with. In practice, think of the spread as the market maker's compensation for bearing inventory and adverse selection risk. Optiver and Virtu interviews often probe this deeply.
Quant recruiting is almost entirely academic, top firms hire from elite PhD programs in mathematics, physics, statistics, computer science, or engineering. Undergrad hires exist but require exceptional academic pedigree.
Key Resources
Search these online to find them:
How to Actually Get Better
Junior Quant (0-3 yrs)
Implement research, run backtests, assist senior researchers.
Quant Researcher (3-6 yrs)
Own alpha research streams, independent model development.
Senior Researcher (6-10 yrs)
Lead teams, develop proprietary strategies, significant P&L.
PM / Head of Research (10+ yrs)
Run systematic strategies with own capital allocation. Carry / large bonuses.
Python (pandas, numpy, scipy)
The essential quant toolkit. Build backtesting frameworks from scratch, implement pricing models, clean messy financial data. Kaggle competitions are the best practice environment.
Probability & Statistics
Understand distributions, hypothesis testing, Bayesian inference, and common statistical traps (overfitting, p-hacking, look-ahead bias). Read 'The Art of Statistics' by David Spiegelhalter.
Stochastic Calculus & Options Pricing
For derivatives quants: understand Brownian motion, Ito's lemma, Black-Scholes, and Monte Carlo simulation. Shreve's 'Stochastic Calculus for Finance' is the definitive text.
Machine Learning
Learn supervised learning (regression, classification), time series models (ARIMA, LSTM), and feature engineering for financial data. Know how to prevent overfitting, this is everything in quant.
C++ (for HFT/performance-critical roles)
Required for high-frequency trading and latency-sensitive roles. Focus on memory management, low-latency data structures, and order book mechanics. Only essential for HFT, not needed for most quant research roles.
AI is a tool quants use, not a replacement. Machine learning IS quant finance in many modern funds. Being a strong quant is increasingly synonymous with being strong in AI/ML, this is the most future-proof role in finance.
Quant researchers who understand machine learning are the people building the AI tools that others fear. The risk is that AI commoditises simpler signal generation, but the deep mathematical and engineering skills required to build robust systematic strategies become more, not less, valuable. The quant field is expanding, not contracting.
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