Quantitative Analyst CV Example
A quantitative analyst CV should showcase your advanced mathematical and programming skills, experience developing financial models, and ability to apply quantitative methods to trading or risk management.
Recommended template: ExecutivePro
Key Skills to Include
Quick Tips
- Highlight your advanced degree in a quantitative discipline such as mathematics, physics, or financial engineering.
- Detail specific models you have developed and their impact on trading or risk management outcomes.
- Include your programming language proficiency and any relevant libraries or frameworks.
- Demonstrate your ability to communicate complex quantitative concepts to non-technical stakeholders.
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Upgrade to ProHow to Write Your Quantitative Analyst CV
A quantitative analyst CV must demonstrate advanced mathematical ability, strong programming skills, and practical experience applying quantitative methods to real financial problems. Employers at banks, hedge funds, and asset managers look for candidates who can develop models that have measurable impact on trading, risk management, or investment processes. Your CV should balance academic credentials with evidence of practical model development and production implementation.
CV Structure
Use a reverse-chronological format with a concise profile highlighting your degree level, areas of quantitative specialism, and key achievements. Place education prominently, particularly if you hold a PhD or MSc in a quantitative discipline. For each role, describe the desk or team, the types of models developed, and the asset classes covered. Include specific technical achievements. Keep the CV to two pages.
CV Format
Use a clean, technical template with clear section headings. Quant CVs should be information-dense but well-organised. List programming languages and technical skills prominently, as these are key screening criteria. Avoid creative designs — quantitative hiring managers value precision and substance over visual flair.
CV Profile Examples
Experienced Quantitative Analyst
PhD-qualified quantitative analyst with six years of experience developing pricing, risk, and trading models within investment banking and hedge fund environments. Expert in stochastic calculus, Monte Carlo simulation, and machine learning techniques applied to derivatives pricing and portfolio optimisation. Proficient in Python, C++, and R with a track record of delivering models that have directly improved trading PnL and risk management accuracy.
Senior Quant — Risk Focus
Mathematically rigorous senior quantitative analyst specialising in market risk modelling and regulatory capital calculation for a tier-one global bank. Experienced in developing VaR, Expected Shortfall, and stress testing models in compliance with FRTB and Basel III requirements. Known for bridging the gap between quantitative research and practical implementation, translating complex mathematical frameworks into production-ready code.
Quantitative Analyst — Early Career
MSc Financial Engineering graduate with two years of experience as a junior quantitative analyst in an equity derivatives desk. Skilled in Python and MATLAB with a strong academic foundation in stochastic processes, numerical methods, and options pricing theory. Eager to develop further in a team that values intellectual rigour and offers exposure to complex modelling challenges across multiple asset classes.
State your degree level, quantitative specialism, and years of industry experience in two to three sentences. Mention the asset classes and model types you work with. Include one technical achievement that demonstrates your impact.
Key Skills for Your Quantitative Analyst CV
Quantitative Modelling
Developing mathematical models for pricing, risk management, and trading strategy across multiple asset classes and product types.
Python / R / MATLAB
Using scientific computing languages for model development, data analysis, and production analytics in financial applications.
Statistical Analysis
Applying statistical methods including regression, time series analysis, and hypothesis testing to financial data.
Stochastic Calculus
Applying Ito calculus, Brownian motion, and stochastic differential equations to derivatives pricing and risk modelling.
Derivatives Pricing
Building pricing models for vanilla and exotic derivatives using analytical solutions, lattice methods, and simulation techniques.
Risk Modelling (VaR, Monte Carlo)
Developing Value-at-Risk, Expected Shortfall, and scenario-based risk models for portfolio and regulatory risk management.
Machine Learning
Applying supervised and unsupervised learning techniques to financial problems including forecasting, classification, and anomaly detection.
C++ / SQL
Writing performance-optimised code in C++ for production systems and using SQL for data extraction and analysis.
Work Experience Examples
For each role, describe the team, the asset classes supported, and the types of models you developed. Include specific methodologies used and their applications. Quantify impact where possible — PnL contributions, risk reduction metrics, or performance improvements. Mention languages and tools used and any cross-functional collaboration with traders or risk managers.
Quantitative Analyst
Citadel Securities
Developed and maintained pricing and risk models for the equity derivatives trading desk, supporting a portfolio with notional exposure exceeding £3 billion.
Responsibilities
- Designed and implemented Monte Carlo pricing engines for exotic equity derivatives including barrier options, autocallables, and worst-of products.
- Developed risk sensitivity calculations (Greeks) and calibrated local volatility surfaces using market data from Bloomberg and internal feeds.
- Built backtesting frameworks to validate model performance against historical market data and identify areas for recalibration.
- Collaborated with traders to understand risk profiles and develop bespoke analytical tools for position management and hedging strategy.
- Maintained production model code in C++ and Python, implementing performance optimisations that reduced pricing run times.
Achievements
- Developed a new local volatility surface calibration approach that reduced pricing error by 18% across the exotic book, contributing to a £2.4M PnL improvement.
- Built a real-time Greeks dashboard in Python that reduced traders' reliance on end-of-day batch processes and improved intraday risk monitoring.
- Published an internal research paper on machine learning applications in volatility forecasting that was presented at the firm's quarterly quant symposium.
Junior Quantitative Analyst
Barclays Investment Bank
Supported the market risk quantitative team in developing and validating risk models for interest rate and credit derivatives portfolios.
Responsibilities
- Implemented VaR and Expected Shortfall calculations using historical simulation and parametric methods in Python and SQL.
- Conducted model validation exercises, testing model assumptions against empirical data and documenting findings for model governance.
- Assisted with stress testing scenario design and implementation in line with PRA and internal risk management requirements.
- Produced model documentation and methodology papers for review by the model risk management function.
Achievements
- Identified a calibration error in the interest rate VaR model that was understating risk by approximately 12%, leading to a model recalibration and improved capital allocation.
- Developed an automated data quality checking tool in Python that reduced manual data validation time by 60%.
Education & Qualifications
Lead with your highest quantitative degree — PhD, MSc, or equivalent — including the institution, subject, and thesis topic if relevant. Follow with your undergraduate degree. Include any relevant coursework in stochastic calculus, numerical methods, or financial engineering. Academic publications or conference presentations should be mentioned.
PhD in Mathematics / Physics / Financial Engineering
Doctoral degree demonstrating advanced research capability in a quantitative discipline directly applicable to financial modelling.
MSc Financial Engineering / Quantitative Finance
Postgraduate qualification combining mathematical methods with financial applications and model development.
CQF (Certificate in Quantitative Finance)
Professional qualification covering derivatives pricing, risk management, and quantitative methods for finance practitioners.
FRM (Financial Risk Manager)
GARP certification demonstrating expertise in financial risk measurement, modelling, and management.
Frequently Asked Questions
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