When a quantitative finance professional loses their job abroad and returns home to find the doors largely shut, the next move is rarely straightforward. Mayank Hedaoo had spent years building a career that cut across engineering, equity research, and climate science.
"I was already working as a quant analyst and as a research engineer. I was working in France," he recalls. "My company got shut down and I had to move back." He had managed passive indexes for firms benchmarked against MSCI and Morningstar, written code for scope-three emissions calculations, and read the quantitative finance canon cover to cover. But re-entering India's quant job market after an overseas stint, he found the gate had narrowed considerably. "In India they only hire from IIT, and outside they need a PhD," he explains. "That is where it was hard to enter."
Rather than waiting out the market, Mayank chose to act. He enrolled in the Executive Programme in Algorithmic Trading (EPAT) at QuantInsti, and within months had accepted a role that, by his own account, exceeded every expectation he had set for himself. Today he trades cotton options and futures at ICE and at exchanges in China, operating in one of the more specialised corners of global commodity markets.
His path from Paris to the trading desk offers a useful illustration of what EPAT can do for someone who already understands the theory but needs the right network and credentials to break through.
Why Algo Trading?
Mayank's interest in quantitative finance did not begin with EPAT. By the time he enrolled, he already had substantial experience on both the research and the implementation side of the field. As a quant analyst, he had worked on index construction and maintenance for some of the most recognised names in passive investing. Scientific Beta, a firm known for building factor-based equity indexes, was among his employers. Before that, his MSC and MBA had given him a thorough grounding in the academic literature that underpins algorithmic strategy development.
His subsequent stint as a research engineer added an unexpected dimension to his profile. The role sat at the intersection of quantitative methods and climate science, focused on calculating scope-three emissions for organisations. "It was a bit quantitative because it involved calculations of scope-three emissions," he notes, adding that the coding and research skills carried over even if the subject matter had shifted. He also observed that the move was not entirely unusual for people with his background: "All the quant firms try to hire those that have a research background, especially in a quantitative field." The experience reflected a broader truth about quantitative professionals: the analytical toolkit is often portable across domains, even when the domain itself is unfamiliar.
When his French employer ceased operations, Mayank found himself re-entering a job market that had become more selective, not less, about academic pedigree for quant research roles. The skills were there; the credential gap and the placement network were not.
Why EPAT?
Mayank cites two reasons for choosing EPAT, and he is precise about both of them. The first was the faculty. For someone who had spent years studying from textbooks, the prospect of engaging directly with the authors behind those texts was a genuine draw. It was not an abstract benefit; it was a chance to resolve questions and hear context that no textbook could fully convey.
"The faculty that is teaching me in this course is actually the same faculty whose books I read in my MSC and MBA."
The second reason was more pragmatic. A college senior had recommended EPAT specifically because of its lifetime placement service. Mayank was candid about his thinking at the time: he calculated that even if the next several months of job searching went nowhere, there was a structured fallback.
"I decided to take this programme for two reasons. One was the faculty. The second reason was the lifetime placement service. I thought that maybe through your placement service I can actually find a job, which I did. I got the job through placement."
That combination, intellectual credibility in the faculty and practical utility in the placement infrastructure, is what made EPAT stand out from the alternatives he considered. For a professional re-entering the market after an involuntary gap, both factors mattered in equal measure.
The EPAT Experience
Mayank came to EPAT with more prior knowledge than the average enrollee. He had spent considerable time on self-directed study before enrolling. "I had read books of Ernie Chan," he says, referring to the widely read practitioner texts on quantitative trading strategies. He had also worked through much of the foundational academic literature by following citation trails from one source to the next. As a result, he found that many modules reinforced rather than introduced concepts.
He is candid about this:
"I wouldn't say I learned anything new across the board."
Where the programme did add value was in areas he had not encountered directly in his work. Translating a strategy from a research notebook into a production-ready system involves a distinct set of engineering considerations, and this was territory Mayank had not mapped in depth before.
"System architecture and stuff, that was something new for me, and options and futures strategies. I had read books, I had learned, but the programme treated them with a rigour that matched what I was expecting from the faculty."
His approach to learning is characteristic of someone who treats any course as a starting point rather than an endpoint. For Mayank, the value of a well-structured programme lies partly in the references it surfaces, the papers and frameworks it points toward, as much as in the content it delivers directly.
"Whenever I'm reading something, then the resources that they cite, I go to those resources."
EPAT provided that signposting alongside the formal curriculum, and for a practitioner already comfortable with the fundamentals, that chain of references was among the most durable parts of the experience.
Life After EPAT
The placement outcome exceeded Mayank's expectations by a considerable margin.
"I'm a bond trader right now," he says. "I'm particularly involved in trading cotton options and futures at ICE and in China."
It is a niche role: cotton derivatives sit in a corner of the commodities market where fundamental knowledge of the physical market intersects with quantitative strategy and risk management across multiple geographies and trading sessions.
His reaction to the outcome was unambiguous. He had entered the placement process after a period of professional disruption, with expectations calibrated to match the difficulty of re-entering the market. The result cleared that bar by a significant margin.
"I'm highly delighted with the company that I'm working with, the process, the payment that I'm getting. Everything was above expectation."
The role also represents a meaningful convergence of his varied background. Bond and commodity trading draws on the quantitative analysis skills he developed as a quant analyst, the research rigour he practised as an engineer, and the options theory he studied both before and during EPAT. The path to this point was not linear, but each stage turns out to have contributed something to the work he does now.
Looking Ahead
Mayank keeps a close eye on where the field is moving, and he is direct about what he thinks matters most in the near term. When asked about topics worth exploring further in algo trading, he did not hesitate.
"Just go deep on MCP and agents. Those are the hot topics right now and very few people are doing it."
Model Context Protocol (MCP) and AI agent frameworks are reshaping how quantitative researchers interact with data, tools, and strategy infrastructure. The ability to build and deploy autonomous agents that can reason over market data, execute research workflows, and adapt to changing conditions is an emerging frontier in systematic trading. Mayank's interest in this area reflects both his technical background and his instinct for where durable skill-building opportunities lie.
For someone who spent time as a research engineer before returning to finance, the convergence of AI tooling and quantitative trading is a natural next area of focus. The skills he built across both domains position him well for the work ahead.
Mayank Hedaoo's story is, in one sense, about resilience: a career interrupted and then rebuilt on firmer ground. But it is also about preparation. He arrived at EPAT already equipped with significant knowledge and used the programme for what he specifically needed, faculty access, structured exposure to systems and derivatives, and a credible route back into the market through placement. "The faculty that is teaching me is actually the same faculty whose books I read," he had said before enrolling. By the time his EPAT journey was over, that statement had become more than a reason to join. It had become a description of how far he had come.
Frequently Asked Questions
Is EPAT suitable for someone who already has a background in quantitative finance?
Can EPAT help with finding a job after a career gap or international relocation?
Do I need a PhD or an IIT background to pursue a career in algo trading?
What is cotton futures trading, and how does it relate to algorithmic trading?
What topics in algo trading are gaining the most traction right now?
How important is it to read beyond the course materials when studying algo trading?
What kind of prior experience is useful before enrolling in EPAT?
How does EPAT's placement service work for international or non-traditional candidates?
Next Steps
If you are just getting started with algorithmic trading, begin with the Quantitative Trading Free Learning Track. It includes beginner-friendly courses covering data basics, trading strategies, and coding for finance.
Once you are ready to go deeper, explore Quantra's Algorithmic Trading for Beginners Learning Track, which offers hands-on, application-focused modules to build your skills step by step.
For those looking for a comprehensive, guided journey with mentorship, live lectures, and career support, the Executive Programme in Algorithmic Trading (EPAT) provides a complete foundation for launching or accelerating a career in this field.
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