Mehul Mehta

This is my third company in the US.

Every time I switched job I had a different strategy in mind.

During my master’s, I was an international student and I had just one goal.

Get a job in the US, no matter what.

So I followed the law of large numbers strategy.

I applied to nearly 2,000 roles and eventually received multiple offers.

My first role in the US was at Regions Bank.

This is where I got my first breakthrough in the USA job market.

The work at Regions was great, but I wanted to move towards market facing modeling roles.

So I started looking jobs which are closer to the markets.

After 1.5 months of searching, I received three offers and chose Charles Schwab, where I worked in Fixed Income Quant for two years.

I feel I had the best time at Charles Schwab as I had exponential learning curve.

Working there, I discovered my real inclination: Derivative pricing and stochastic modeling.

So, I started looking for the next role and my strategy changed again.

No mass applications.

Only a fixed number of highly focused applications.

And once again, I received 3 offers.

Here is the biggest lesson.

Career advice is never universal.

Some seniors will tell you to apply everywhere.

Others will say apply selectively.

Both can be right.

Because the correct strategy depends on your stage of life.

If you are an international student in the US, your first priority is simple.

Get a job in the system.

You can optimize later.

You can specialize later.

You can move closer to your dream role later.

But first, survive, stabilize, and enter the market.

Everything else follows from there.

3 days ago | [YT] | 145

Mehul Mehta

Happy to share that I’ve passed the FRM Level 1 exam 😄😄

This journey involved solving 1000+ practice questions, revisiting concepts multiple times, and truly understanding the intuition behind risk models rather than memorizing formulas.

FRM Level 1 is a strong foundation, but this is just the beginning. 💪💪

Looking forward to Level 2

1 month ago | [YT] | 153

Mehul Mehta

Quant Firms in India 🇮🇳

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2 months ago | [YT] | 266

Mehul Mehta

Algorithmic Trading: The world where mathematics meets markets

Most people think algo trading is only about writing a “fast code.”

In reality, it is a combination of statistics, market microstructure, execution science, and risk management.

Here is how I explain it in one line

Algo Trading is using rules, data and automation to execute trades faster and more efficiently than humans.

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2 months ago | [YT] | 80

Mehul Mehta

Quant Roles explained 💯

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3 months ago | [YT] | 146

Mehul Mehta

Over the years I’ve worked with some of the best Quants in the industry. 💯

What makes them stand out are not just models and math, but the habits they live by:

1. Discipline like traders — structured days, focused routines

2. Attention to detail — checking data twice, assumptions thrice

3. Curiosity beyond finance — physics, CS, probability, even philosophy

4. Reading research daily — they stay ahead by learning continuously

5. Coding relentlessly — always building, testing, automating

6. Questioning everything — never taking inputs or outputs at face value

7. Explaining simply — breaking down complex math into clear insights

8. Staying calm under pressure — markets move fast, but they stay grounded

9. Networking with purpose — exchanging ideas with academics, traders, and engineers

10. Balance of speed & depth — quick to act, deep when solving problems

At the end of the day — habits build great Quants, not just equations.

6 months ago | [YT] | 91

Mehul Mehta

Stochastic Modeling 😂😂

6 months ago | [YT] | 52

Mehul Mehta

🎓 Is a Master’s in Financial Engineering (MFE) worth it in 2025?

I get this question from students and professionals all the time — and my answer is simple:

✅ Yes. 100% worth it — especially if you are targeting the U.S. market.

Let me explain why 👇


🇺🇸 1. The U.S. Quant Finance Market Is Massive

Whether it’s hedge funds, asset managers, banks, or prop trading firms, the demand for skilled quants has never been higher.

From pricing exotic derivatives to building risk models, from algo trading to credit risk modeling — the U.S. market has a job for every quant specialization.

The best part?
You don’t need to be from an Ivy League or “top 10” university.

Many firms care more about your:
📌 Practical skills
📌 Projects and GitHub
📌 Understanding of financial products


💰 2. The Degree is Expensive — But ROI is High

Yes, MFEs in the U.S. can cost $60,000–$90,000.

But if you land a full-time role as a quant, quant dev, or model validator, you’re typically starting with a base salary of $100K+, not including bonuses.

📌 Many students recover the full tuition cost in 1–2 years.

Plus, the U.S. allows you to work during and after your degree (OPT/STEM OPT), so you can gain experience and earn while you learn.


📚 3. The Learning Experience Is Intense — and Worth It

In an MFE program, you’ll dive deep into:

➡️ Derivatives Pricing
➡️ Risk Management
➡️ Numerical Methods
➡️ Machine Learning for Finance
➡️ Stochastic Calculus
➡️ Portfolio Optimization
➡️ Python, R, C++, Excel Modeling

It’s not easy — but if you stay consistent, you’ll come out job-ready.


💡💡My Take?

If you’re serious about a career in Quantitative Finance, want to build globally relevant skills, and are ready to work hard — an MFE from the U.S. is one of the best investments you can make in 2025.

Don’t worry if you’re not from IIT, IIM, or a top-tier college.

Quant finance rewards curiosity, consistency, and competence.

So if you’re dreaming of becoming a quant — this might be your moment.

I did it.
Thousands of others have.
You can too.

7 months ago | [YT] | 69

Mehul Mehta

If going for Quant Interviews, please make sure to revise all the below concepts 💯

📘 Derivatives

→ Forwards and Futures
→ Options (Call & Put)
→ Call & Put Options Payoffs
→ European vs American Options
→ Put-Call Parity
→ Option Greeks (Delta, Gamma, Vega, Theta, Rho)
→ Black-Scholes Model
→ Binomial/Trinomial Trees
→ Monte Carlo Pricing
→ Volatility Smile & Surface
→ Exotic Options: Barrier, Asian, Lookback, Binary
→ Swaps: Interest Rate & Equity Swaps
→ Implied Volatility
→ Hedging Strategies & Real-world Use Cases

💵 Fixed Income

→ Bond Pricing, YTM, Spot/Forward Rates
→ Duration, Modified Duration, Convexity
→ Bootstrapping Yield Curve
→ Term Structure Models (Nelson-Siegel, Svensson)
→ Interpolation (Linear, Cubic Spline, Monotone Convex)
→ Interest Rate Derivatives (Caps, Floors, Swaptions)
→ Z-Spread, Option-Adjusted Spread (OAS)
→ Mortgage-Backed Securities (MBS), ABS
→ Prepayment Risk (CPR, PSA, SMM)
→ Repo, Reverse Repo
→ Key Rate Duration
→ Interest Rate Models: Vasicek, CIR, Hull-White, BGM

📉 Market Risk

→ Value at Risk (VaR): Historical, Parametric, Monte Carlo
→ Expected Shortfall (CVaR)
→ Volatility Modeling: EWMA, GARCH
→ Risk Sensitivities: Greeks, DV01, PV01
→ Full Revaluation vs Delta-Normal VaR
→ Stress Testing & Scenario Analysis
→ Marginal & Incremental VaR
→ P&L Attribution
→ Backtesting VaR
→ Capital Models (Basel, FRTB)
→ Liquidity Risk and Market Data Mapping
→ Sensitivity Analysis (IR, FX, Credit, Equity)

🔢 Stochastic Calculus

→ Brownian Motion
→ Ito’s Lemma
→ Geometric Brownian Motion
→ Stochastic Differential Equations (SDEs)
→ Martingales
→ Risk-Neutral Valuation & Girsanov’s Theorem
→ Black-Scholes Derivation from SDE
→ Jump Diffusion Models (Merton)
→ Heston Model
→ SABR Model
→ Numerical Methods: Euler, Milstein
→ Feynman-Kac Theorem
→ Applications to Derivatives & Interest Rate Modeling

⏱️ Time Series Analysis

→ Stationarity and Unit Root Tests
→ Autocorrelation, Partial Autocorrelation (ACF, PACF)
→ AR, MA, ARMA, ARIMA, SARIMA
→ ARCH, GARCH, EGARCH, TGARCH
→ Volatility Clustering
→ Rolling Mean & Rolling Volatility
→ Seasonality & Trend Detection
→ Forecast Accuracy: MAPE, RMSE
→ Cointegration and Error Correction Models
→ Kalman Filter
→ VAR Models
→ Application: Forecasting asset returns, volatility, macro variables

🤖 Machine Learning in Quant Finance

→ Supervised vs Unsupervised Learning
→ Feature Engineering for Financial Data
→ Regression Models (Linear, Lasso, Ridge)
→ Classification Models (Logistic, Decision Tree, SVM)
→ Ensemble Methods (Random Forest, XGBoost)
→ Time Series ML (Lag features, Rolling stats)
→ Clustering: K-Means, DBSCAN
→ Dimensionality Reduction: PCA, t-SNE
→ Cross-Validation Techniques (K-Fold, TimeSeriesSplit)
→ Model Evaluation: AUC-ROC, Precision, Recall, F1
→ Overfitting/Underfitting, Regularization
→ Use Cases: Credit Risk Modeling, Algo Trading, Fraud Detection, Price Prediction

PS: Make sure to practice a lot of probability and puzzles from Green Book 📗

7 months ago | [YT] | 95

Mehul Mehta

🚫 Planning to pursue a Master’s in Financial Engineering (MFE) in the U.S. with ZERO work experience?

Please read this carefully

It might save you from a tough journey ahead.

➡️ If you’re a fresher with no industry exposure

➡️ If you think just landing in the U.S. will create magic

➡️ If you’re relying only on your degree to get interviews

⚠️ You’re setting unrealistic expectations.

I’m saying this because I’ve personally seen so many of my juniors struggle.

🎯 No work experience =

❌ No interview calls
❌ No clarity on what roles to apply for
❌ No strong resume to stand out

💡 My honest advice?

✅ Gain at least 1–2 years of work experience before you apply.

✅ Ideally in finance, data, tech, or any analytical role.

Here’s what experience gives you:

🔹 Stronger understanding of real-world finance
🔹 Better projects and talking points for interviews
🔹 More clarity on your goals
🔹 A competitive edge in the U.S. job market

🎓 An MFE is valuable — but experience is what makes it work for you.

Don’t come expecting miracles.

Come prepared.

Two years of work now can save you two years of struggle later.

#MFE #QuantFinance #CareerAdvice #InternationalStudents #FinancialEngineering #MastersAbroad #JobSearch #QuantCareers #MSinUSA

8 months ago | [YT] | 38