Most students want to enter Quant Finance because of the high salaries.
But here’s a question worth asking yourself:
If the same job paid a low salary, would you still be interested in it?
It’s an uncomfortable question, but an important one.
Because if the honest answer is no, then you might be chasing the outcome, not the craft.
And in fields like quant finance, chasing outcomes rarely works in the long run.
Quant finance is not just about compensation.
At its core, it is about: 👉 curiosity for mathematics and statistics 👉 building models to understand uncertainty 👉 thinking deeply about risk and probabilities 👉 working with messy real-world data 👉 continuously learning new tools, methods, and ideas
The people who thrive in this field are usually the ones who genuinely enjoy the process of solving difficult problems.
Not just the paycheck at the end of the month.
Another reality that many students don’t talk about enough:
Careers rarely start at their peak.
Many professionals in quant, finance, tech, and research started with: 👉 modest salaries 👉 junior roles 👉 long learning curves 👉 and plenty of mistakes
And that’s perfectly normal.
There is no shame in starting small early in your career if you are learning the right things.
In fact, the early years should ideally be focused on learning and skill building, not maximizing salary.
Because over time something powerful happens:
Skills compound.
If you spend the first few years focusing on: • strong foundations in statistics, probability, and programming • real problem-solving experience • understanding markets and risk • building projects and models • learning how to think critically
then opportunities tend to follow.
The people who become valuable in this field are not necessarily the ones who started with the highest salaries.
They are the ones who became exceptionally good at what they do.
Money often follows people who build rare and valuable skills.
Not the other way around.
So if you are entering quant finance, ask yourself:
Do I enjoy the learning process even when it’s difficult?
Do I like thinking about models, data, and uncertainty?
If the answer is yes, then you are on the right path.
And if the salary is small at the beginning, that’s okay.
Because careers are long, and compounding works in skills just like it works in finance.
A few years ago, I had absolutely no idea what quantitative finance really meant.
Terms like stochastic processes, volatility modeling, and derivatives pricing sounded extremely intimidating.
The formulas looked complex, and honestly, the whole field felt difficult to even approach.
But curiosity kept pushing me.
I started reading more about financial markets, experimenting with different models, and trying to understand how mathematics is used to explain market behavior.
At first, many things didn’t make sense.
But over time, concepts slowly started becoming clearer.
What fascinated me the most was realizing that financial markets, which often look chaotic and unpredictable, can actually be studied through mathematical frameworks.
Models may not perfectly predict the future, but they help us understand risk, uncertainty, and market dynamics much better.
Looking back, the most important thing in this journey wasn’t knowing everything from the beginning.
It was simply staying curious long enough to keep exploring.
And the interesting part about this field is that the learning never really stops.
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. 💪💪
Mehul Mehta
Most students want to enter Quant Finance because of the high salaries.
But here’s a question worth asking yourself:
If the same job paid a low salary, would you still be interested in it?
It’s an uncomfortable question, but an important one.
Because if the honest answer is no, then you might be chasing the outcome, not the craft.
And in fields like quant finance, chasing outcomes rarely works in the long run.
Quant finance is not just about compensation.
At its core, it is about:
👉 curiosity for mathematics and statistics
👉 building models to understand uncertainty
👉 thinking deeply about risk and probabilities
👉 working with messy real-world data
👉 continuously learning new tools, methods, and ideas
The people who thrive in this field are usually the ones who genuinely enjoy the process of solving difficult problems.
Not just the paycheck at the end of the month.
Another reality that many students don’t talk about enough:
Careers rarely start at their peak.
Many professionals in quant, finance, tech, and research started with:
👉 modest salaries
👉 junior roles
👉 long learning curves
👉 and plenty of mistakes
And that’s perfectly normal.
There is no shame in starting small early in your career if you are learning the right things.
In fact, the early years should ideally be focused on learning and skill building, not maximizing salary.
Because over time something powerful happens:
Skills compound.
If you spend the first few years focusing on:
• strong foundations in statistics, probability, and programming
• real problem-solving experience
• understanding markets and risk
• building projects and models
• learning how to think critically
then opportunities tend to follow.
The people who become valuable in this field are not necessarily the ones who started with the highest salaries.
They are the ones who became exceptionally good at what they do.
Money often follows people who build rare and valuable skills.
Not the other way around.
So if you are entering quant finance, ask yourself:
Do I enjoy the learning process even when it’s difficult?
Do I like thinking about models, data, and uncertainty?
If the answer is yes, then you are on the right path.
And if the salary is small at the beginning, that’s okay.
Because careers are long, and compounding works in skills just like it works in
finance.
#QuantFinance #CareerAdvice #Students #FinanceCareers #Learning #LongTermThinking
2 days ago | [YT] | 53
View 2 replies
Mehul Mehta
Everyone talks about the importance of staying loyal to one firm for a long time.
And yes, stability has its value.
But there is something people often ignore.
Staying in the same role for 10 years while doing the same thing repeatedly, without learning anything new, can be far more damaging to a career.
Early in your career, the most valuable thing is not the number of years you spend at a company.
It is how much you grow during those years.
Over time, changing firms allowed me to work on a wide range of problems and expand my understanding of quantitative finance.
Through different roles and projects, I have had the opportunity to learn and work on areas such as:
• Fixed Income analytics
• Equity Derivatives
• Time Series Modeling
• Stochastic Volatility Models
• Stochastic Interest Rate Models
• Building end-to-end automation frameworks
Each transition came with new challenges, new systems, new teams, and most importantly, new learning curves.
And that learning compounds over time.
Changing firms should not be about chasing titles or switching for the sake of switching.
It should be about finding environments where you can continue to grow, build, and expand your skill set.
Because at the end of the day, a career is not defined by how long you stayed somewhere.
It is defined by how much you learned along the way.
#QuantFinance #CareerGrowth #Learning #FinanceCareers #ProfessionalDevelopment
3 days ago | [YT] | 35
View 1 reply
Mehul Mehta
A few years ago, I had absolutely no idea what quantitative finance really meant.
Terms like stochastic processes, volatility modeling, and derivatives pricing sounded extremely intimidating.
The formulas looked complex, and honestly, the whole field felt difficult to even approach.
But curiosity kept pushing me.
I started reading more about financial markets, experimenting with different models, and trying to understand how mathematics is used to explain market behavior.
At first, many things didn’t make sense.
But over time, concepts slowly started becoming clearer.
What fascinated me the most was realizing that financial markets, which often look chaotic and unpredictable, can actually be studied through mathematical frameworks.
Models may not perfectly predict the future, but they help us understand risk, uncertainty, and market dynamics much better.
Looking back, the most important thing in this journey wasn’t knowing everything from the beginning.
It was simply staying curious long enough to keep exploring.
And the interesting part about this field is that the learning never really stops.
Still learning.
Still exploring.
#QuantFinance #Derivatives #Volatility
1 month ago | [YT] | 115
View 6 replies
Mehul Mehta
The world is changing faster than most people realize.
Bloomberg just introduced ASKB, bringing agentic AI directly into the Bloomberg Terminal.
This is not just a chatbot upgrade.
This is AI embedded into core investment workflows, capable of pulling data, research, analytics, and generating structured insights in real time.
This signals something bigger.
Agentic AI is moving from experimentation to production across financial institutions.
Firms like BlackRock are integrating AI into portfolio construction and risk systems.
JPMorganChase is deploying AI internally across research and trading workflows.
Even major exchanges and data providers are embedding AI directly into analytics infrastructure.
We are entering a phase where AI does not just assist.
It monitors, reasons, and acts within financial systems.
In investment, trading, and risk management, this will fundamentally change how decisions are made.
The question is no longer whether AI will be adopted.
The question is how fast institutions adapt.
1 month ago | [YT] | 31
View 5 replies
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.
1 month ago | [YT] | 148
View 8 replies
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] | 154
View 12 replies
Mehul Mehta
Quant Firms in India 🇮🇳
3 months ago | [YT] | 266
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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.
3 months ago | [YT] | 80
View 2 replies
Mehul Mehta
Quant Roles explained 💯
4 months ago | [YT] | 146
View 7 replies
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.
7 months ago | [YT] | 92
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