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Your CV can open doors - or close them. Yet many quants make the same costly mistakes when writing theirs. We’ve highlighted the top 3 CV mistakes quants make, and how to get rid of them.
Mistake 1: Irrelevant Keywords
Too many resumes are packed with corporate finance terms (DCF, capital budgeting, due diligence). These don't matter for quant roles.
Instead, use keywords aligned with your target track:
- Trading → low-latency, market microstructure, execution, slippage
- Research → factor models, Bayesian inference, ensemble methods, feature engineering
- Risk → VaR, CVaR, liquidity risk, stress testing, counterparty risk
Mistake 2: Keyword Stuffing Without Context
Listing "Python, SQL, ML" means nothing on its own.
Use the formula:
Skill → Action → Method → Result
Example:
Engineered predictive features from intraday order book data using Pandas & NumPy → improved profitable trade classification accuracy by 9%
Mistake 3: No Metrics
"Built an ARIMA model" is not enough.
Always quantify:
- Automated daily VaR reports → cut reporting time 2h → 30min
- Forecasting model → reduced error by 8% vs rolling average
- Feature engineering → increased model accuracy by 9%
Useful metrics: Sharpe ratio, drawdown, % speed improvement accuracy, $ savings, number of users.
Takeaway:
Your quant resume should convince a recruiter in under 30 seconds that you're job-ready:
- Relevant keywords
- Skill + Action + Method + Result
- Real, measurable impact
Mistake 1: Irrelevant Keywords
Too many resumes are packed with corporate finance terms (DCF, capital budgeting, due diligence). These don't matter for quant roles.
Instead, use keywords aligned with your target track:
- Trading → low-latency, market microstructure, execution, slippage
- Research → factor models, Bayesian inference, ensemble methods, feature engineering
- Risk → VaR, CVaR, liquidity risk, stress testing, counterparty risk
Mistake 2: Keyword Stuffing Without Context
Listing "Python, SQL, ML" means nothing on its own.
Use the formula:
Skill → Action → Method → Result
Example:
Engineered predictive features from intraday order book data using Pandas & NumPy → improved profitable trade classification accuracy by 9%
Mistake 3: No Metrics
"Built an ARIMA model" is not enough.
Always quantify:
- Automated daily VaR reports → cut reporting time 2h → 30min
- Forecasting model → reduced error by 8% vs rolling average
- Feature engineering → increased model accuracy by 9%
Useful metrics: Sharpe ratio, drawdown, % speed improvement accuracy, $ savings, number of users.
Takeaway:
Your quant resume should convince a recruiter in under 30 seconds that you're job-ready:
- Relevant keywords
- Skill + Action + Method + Result
- Real, measurable impact
