Build the systems and models that trading firms use to make money. Hard to enter, concentrated, very well paid.
Edited by the Canvas Classes editorial team · last reviewed 2026-04
p75 numbers are heavily bonus-weighted — base salaries are typically high but the variable component (bonus tied to firm performance + team performance + individual output) often equals or exceeds base. At top HFT firms, exceptional individual contributors at year 8-10 hit ₹5Cr+ total comp. p25 reflects sell-side quant roles (banks) which are more salary-stable but lower-ceiling. Compensation is HIGHLY firm-dependent: the top 3-4 firms pay 2-3x what the bottom 3-4 in the same career pay.
"Quant developer / quantitative analyst" splits into distinct sub-paths in 2026 — each with different AI exposure and pay. The sub-path you choose matters more than the parent career name.
Writes the production trading software — low-latency C++, exchange-protocol parsers, order management systems. Most common entry point. Heavy software engineering, lighter math.
Generates and tests trading hypotheses using statistical + ML methods. PhD-track common; MS in stats / financial engineering / CS-with-strong-math also viable. The highest-paid sub-path at top firms.
Operates trading strategies in production, monitors performance, handles edge cases. Smaller subset of roles. Requires comfort with intra-day risk + decision-making under uncertainty.
Builds models that measure + control trading risk. Common at investment banks. Less glamorous than research / trading but more stable.
Quant roles at investment banks (Goldman, Morgan Stanley, JP Morgan, Citi). Salary-heavy compensation, more stable, somewhat lower ceiling than HFT / hedge funds.
B.Tech Mathematics & Computing (any IIT) · B.Tech CSE with strong math performance · B.Tech ECE with strong math performance
Year 1-2: Math + stats + CS fundamentals deeply. CP if accessible (CodeChef, Codeforces) — most quant firms care about CP background.
Year 3: Apply to quant firm internships aggressively — Tower Research, Jane Street, Optiver, Da Vinci all run intern programs. Internship is the dominant entry path.
Year 4: Convert internship to return offer OR apply directly. Interview prep should consume 200+ hours minimum — most candidates underestimate this. Practise math problems on whiteboard.
Throughout: read books mentioned above, build a small portfolio of reproduced strategies, keep CP rating reasonable, maintain CGPA (top firms screen on CGPA).
Any decent math-heavy engineering degree + 12+ months of focused quant interview prep + serious portfolio of reproduced quant strategies + 1-2 internships at quant firms during years 3-4. Unusual path but has happened: students from non-IIT colleges have broken into HFT firms via excellent interview performance + portfolio + cold outreach. The entry bar is genuinely high for top firms but somewhat lower for sell-side bank quant roles.
The defining skill of quant work. Engineers who deeply understand probability — Bayes' theorem under pressure, distributions and their tails, conditional expectations, stochastic processes — pass interview rounds that filter out 80 % of applicants. AI tools don't replace this; they amplify it for people who have it.
HFT firms run their trading software in C++. Engineers who can write tight, cache-friendly, lock-free C++ — and read OTHER people's C++ at scale — pass technical rounds that pure-Python engineers can't. Modern C++ (C++17 / 20) is a meaningfully different skill than Python.
The quants who progress past entry-level are the ones who can read a research paper from the Journal of Finance or QF and figure out whether it's actually useful in production. This is a synthesis skill — math + engineering judgment + market understanding.
Quant interview rounds + the actual job both feature problems where there's no clean answer + you have to make a decision under time pressure. Engineers who freeze under ambiguity don't pass quant interviews. The skill is genuinely trainable but you have to lean into it deliberately.
~200-300 entry-level quant roles in India per year against ~5,000+ serious applicants. Even strong candidates from top IITs get rejected. Plan for this — have a backup plan (SWE or MLE or data engineering offer) before you commit fully to quant interviewing. Don't treat quant as your default; treat it as your stretch with a real fallback.
Within the "quant" job title, working at a top HFT firm vs a mid-tier sell-side desk is the difference between ₹50L and ₹15L entry comp. Research which firm is actually paying what BEFORE accepting offers. Glassdoor + Levels.fyi + Quora threads + cold-outreach to current employees are all useful.
HFT firms close, reorganise, or downsize teams more frequently than software companies. Even good firms have years where they shed 30 % of headcount. A 10-year quant career might involve 2-3 firm changes — sometimes by choice, sometimes not. Compare to SWE-product where job mobility is a feature, here it can be a forced reality.
Quants think hard for a living. Engineers who like to "vibe code" or do straightforward CRUD work find quant exhausting. The intellectual intensity is part of the appeal for the right person, but it's real — burnout rates are higher than in standard product engineering.
Outside these two cities, real quant roles are rare. If you cannot relocate, plan accordingly. Sell-side quant roles at investment banks have slightly more geographic diversity (Pune, Hyderabad teams exist) but the HFT employers are nearly entirely Bangalore + Mumbai.
Many quant researchers pivot to ML engineering at tech companies — same math + programming foundation, lower variance, broader geography.
Quant developers (engineering-track) pivot to SWE-product readily — the C++ + systems skills transfer. Lower comp but broader career flexibility.
Risk quants + sell-side quants who decide finance isn't for them pivot to data engineering — overlapping data + systems skills.
During college: IIT Bombay Mathematics & Computing. Strong CP background (4-star CodeChef). Internship at Tower Research Mumbai in year 3. Return offer for quant developer role.
Now: Senior quant developer at Tower Research, 5 years experience
During college: IIT Madras CSE. Strong math performance (CGPA 9.5+, math electives). Spent 300+ hours on quant interview prep + Project Euler + Putnam-style problems. Internship at Jane Street Hong Kong + summer internship at Da Vinci Bangalore in year 3.
Now: Quant researcher at Da Vinci Trading Bangalore, 3 years experience
During college: NIT Trichy CSE. CGPA 9.3, strong CP background. Did NOT get a quant internship in year 3 despite trying. Spent 8 months in year 4 on intensive quant interview prep + reproduced 3 quant strategies on GitHub. Got into a sell-side quant role at a Mumbai bank on the 4th application attempt.
Now: Quant analyst at a Mumbai investment bank, 2 years experience
At year five, the median Quant developer / quantitative analyst earns around ₹1.1 cr, with the 25th percentile at ₹60 LPA and the 75th percentile at ₹2.2 cr. The distribution widens further at year ten as senior roles diverge from generalist ones. Numbers reflect 3 cited sources last refreshed 2026-04.
The primary undergraduate route is B.Tech Mathematics & Computing (any IIT), B.Tech CSE with strong math performance, B.Tech ECE with strong math performance. Most graduates reach their first meaningful income around 4 years after class 12. The full brief covers stretch, realistic, and accessible target colleges plus the minimum-viable path for students who don't reach a top-tier institution.
No career is fully AI-proof. Our five-year assessment for Quant developer / quantitative analyst is low exposure — the work is largely resistant to AI compression (medium confidence). Quant work is one of the more AI-resistant tech careers because the value is in PROPRIETARY research + low-latency execution — both genuinely hard for general AI tools to replicate. Quant firms are themselves among the heaviest AI users; this is more of a "AI augments quants" story than "AI replaces quants". The role's deeper risk is not AI — it's market regime changes that obsolete whole strategies, plus firm-level survival risk (HFT firms close down regularly when their edge erodes).
Entry is genuinely narrow. ~200-300 entry-level quant roles in India per year against ~5,000+ serious applicants. Even strong candidates from top IITs get rejected. Plan for this — have a backup plan (SWE or MLE or data engineering offer) before you commit fully to quant interviewing. Don't treat quant as your default; treat it as your stretch with a real fallback. The full brief lists every downside our editorial team named — we don't publish a career without them.
Natural pivots include Ml Engineer, Software Engineer Product, Data Engineer. Each one shares a meaningful overlap in skills, training, or work texture, so the transition cost is lower than starting over. The full brief explains the specific overlap for each pivot.
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