Recruiting season is here, so here are my top 3 content mistakes I see on MFE and new-quant resumes — plus how to fix them.
To keep things concise and clear, I used ChatGPT to draft and refine my examples — but the common mistakes and recommendations you see here come from 5 years of reviewing MFE and quant resumes, including my experience working at MSCF in Career Services.
Mistake #1: Overemphasis on fundamental and/or irrelevant keywords.
Instead: Focus on relevant quant experiences, projects, skills, and techniques.
Your resume should look like the role you want. At first glance, I should be able to easily identify concepts and keywords related to stats, math, data science, and finance.
It's ideal to match the keywords to the job description itself, but you can put several into ChatGPT and ask it to identify the top 10 most common skills across those job postings and list examples. Here are some role-specific keywords to get you started:
Mistake #2: Technical “keyword stuffing” without context.
Instead: Understand WHY your reader cares about keywords. Show the skill, what you did with it, how you did it, and the result.
This is taking my advice about "finding the right keywords" to the extreme. You do NOT need 100% keyword match to "get past" the ATS. (If you're not sure what an ATS looks like to a recruiter, check out this video by my favorite resume writer, Sam Struan.)
You're writing for two audiences - the recruiters (generalists) scanning for keywords that match the job description (and might spend 6 seconds on a first glance at your resume) and the hiring managers who want proof you understand them.
Example: Recruiter sees time series --> The manager wants to see ARIMA, GARCH, volatility modeling, and how you cleaned the data, interpreted it, and ensured its accuracy.
What to include in each bullet:
Mistake #3: Missing relevant metrics.
Instead: Quantify your achievements and offer context.
Numbers prove credibility, show scale, and make decisions easier for employers. Just like QuantNet Rankings help you decide which schools you want to apply for without you having to spend hours collecting data from every program page.
Examples:
Automated daily VaR and stress test reports in Excel and SQL -- this is okay, but when you add the result to it, it's more interesting -- cutting reporting time from 2 hours to 30 minutes and improving accuracy by eliminating manual data entry.
Developed an ARIMA volatility forecasting model in R for S&P 500 index options
-- when a result is tied to this, I can start visualize how you can save my firm money -- reducing forecast error by 8% compared to a 30-day rolling average.
Useful metrics:
The Big Takeaway?
Your resume should tell a recruiter or hiring manager you’re the right hire in under 10 seconds:
Ready for Your Next Steps?
My Quant Resume Labs start next week and spots are filling up fast!
In this market, speed matters and you need a strong resume ASAP. The resume lab gives you a chance to get actionable feedback in a small group setting from fellow MFEs, aspiring quants, and me - allowing you to make changes and start applying quickly!
Need a personalized recruiting or grad school strategy? Book a free consult or a one-time career coaching session (20% off). We’ll map out how you can get organized, avoid overwhelm, and stay ahead of both deadlines and your competition.
Feel free to connect with me on LinkedIn for more content like this!
To keep things concise and clear, I used ChatGPT to draft and refine my examples — but the common mistakes and recommendations you see here come from 5 years of reviewing MFE and quant resumes, including my experience working at MSCF in Career Services.
Mistake #1: Overemphasis on fundamental and/or irrelevant keywords.
Instead: Focus on relevant quant experiences, projects, skills, and techniques.
Your resume should look like the role you want. At first glance, I should be able to easily identify concepts and keywords related to stats, math, data science, and finance.
- ML & Stats: linear and logistic regression, random forest, time series, clustering, XGBoost, PCA
- Core Quant: backtesting, Monte Carlo, stochastic processes, option pricing, statistical arbitrage, yield curve, risk-neutral, signal generation, etc.
It's ideal to match the keywords to the job description itself, but you can put several into ChatGPT and ask it to identify the top 10 most common skills across those job postings and list examples. Here are some role-specific keywords to get you started:
- Quant Trading: Focus on execution speed, strategy deployment, and market interaction
microstructure, low-latency, high frequency, algo trading strategies, slippage, execution, equities, rates, bid/ask spread
- Quant Research: Focus on finding patterns, building models, and creating signals
factor research, Bayesian inference, ensemble methods, feature engineering, model validation, hypothesis testing, alternative datasets
- Quant Risk: Focus on measuring, managing, and reporting financial risk
Value-at-Risk, CVaR, liquidity risk, market risk, counterparty risk, stress testing, macroeconomics, credit exposure, sensitivity/scenario analysis
Mistake #2: Technical “keyword stuffing” without context.
Instead: Understand WHY your reader cares about keywords. Show the skill, what you did with it, how you did it, and the result.
This is taking my advice about "finding the right keywords" to the extreme. You do NOT need 100% keyword match to "get past" the ATS. (If you're not sure what an ATS looks like to a recruiter, check out this video by my favorite resume writer, Sam Struan.)
You're writing for two audiences - the recruiters (generalists) scanning for keywords that match the job description (and might spend 6 seconds on a first glance at your resume) and the hiring managers who want proof you understand them.
Example: Recruiter sees time series --> The manager wants to see ARIMA, GARCH, volatility modeling, and how you cleaned the data, interpreted it, and ensured its accuracy.
What to include in each bullet:
- Skill: backtesting, feature engineering, risk modeling
- What you did: created a tool, tested a hypothesis, automated a process
- How you did it: programming languages, algorithms, models, or strategies
- Impact: measurable outcome (see Mistake #3)
- Engineered predictive features from intraday order book data using Pandas and NumPy; improving model accuracy in classifying profitable trades by 9%.
- Built and trained a random forest machine learning model in Python to predict credit risk from open-source financial datasets, achieving 78% accuracy on unseen test data.
Mistake #3: Missing relevant metrics.
Instead: Quantify your achievements and offer context.
Numbers prove credibility, show scale, and make decisions easier for employers. Just like QuantNet Rankings help you decide which schools you want to apply for without you having to spend hours collecting data from every program page.
Examples:
Automated daily VaR and stress test reports in Excel and SQL -- this is okay, but when you add the result to it, it's more interesting -- cutting reporting time from 2 hours to 30 minutes and improving accuracy by eliminating manual data entry.
Developed an ARIMA volatility forecasting model in R for S&P 500 index options
-- when a result is tied to this, I can start visualize how you can save my firm money -- reducing forecast error by 8% compared to a 30-day rolling average.
Useful metrics:
- Performance: Sharpe ratio, max drawdown, R², alpha/beta
- Efficiency: % improvement in speed, accuracy, or automation
- Risk/Cost: drop in volatility, operating costs, or error rate
- Reach: number of users, teams, or departments using your tool/model
- Competitions: placement compared to total participants
- Financial: % increase in returns, $ savings/profit generated
- Sharpe ratio of 12 — without saying it came from a short and carefully chosen backtest
- 95%+ accuracy predicting stock prices — with no time horizon or out-of-sample proof
- $5M P&L gain— from a paper trading sim with no transaction costs or slippage
The Big Takeaway?
Your resume should tell a recruiter or hiring manager you’re the right hire in under 10 seconds:
- Mistake #1: Packing your resume with irrelevant or generic terms.
How to Fix It: Use keywords that match your target role and remove what’s not relevant.
- Mistake #2: Listing skills without showing how you applied them.
How to Fix It: Give context — skill, action, techniques/methods, and result.
- Mistake: Leaving out numbers that prove your impact.
How to Fix It: Back it up with metrics to show scope, scale, and impact.
Ready for Your Next Steps?
My Quant Resume Labs start next week and spots are filling up fast!
In this market, speed matters and you need a strong resume ASAP. The resume lab gives you a chance to get actionable feedback in a small group setting from fellow MFEs, aspiring quants, and me - allowing you to make changes and start applying quickly!
Need a personalized recruiting or grad school strategy? Book a free consult or a one-time career coaching session (20% off). We’ll map out how you can get organized, avoid overwhelm, and stay ahead of both deadlines and your competition.
Feel free to connect with me on LinkedIn for more content like this!
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