- Joined
- 8/27/24
- Messages
- 12
- Points
- 13
Hi everyone,
I’m a recent undergraduate student from one of the old top 5 IITs in India, with a B.Tech from Mechanical Engineering Department. While my major wasn’t directly related to finance, I’ve spent the last 3 years building toward a career in quantitative finance and trading - through internships, real world quant research, and competitions. I’ve been meaning to apply for a MFE since last year. But to be honest, I didn’t feel confident that my profile was competitive enough during the last application cycle (December '24). So instead of rushing, I decided to focus on building further credibility.
Details about my Academic Performance :
CGPA: 7.64/10 (~3.27/4 scale)
While not stellar, my GPA reflects a split between my disinterest in the mechanical engineering core and my deepening focus on quantitative finance. I prioritized internships, competitions, and hands-on learning. This trade-off cost me academically.
Relevant math coursework:
Advanced Calculus* – 10/10
Linear Algebra, Numerical & Complex Analysis** – 10/10
Probability & Statistics – 6/10 (This was the one weak spot, caused by a health issue at the time. If needed, I’m open to retaking a formal course in this area to strengthen my academic narrative.)
Certifications :
I cleared CFA Level 1
I’ve registered for FRM Part 1 (August 2025 attempt)
I plan to take the GRE, TOEFL, and IELTS right after that
I will also be enrolling in the C++ Programming for Financial Engineering course offered by QuantNet and Baruch College
Awards: I was also honored with the Institute Order of Merit in Technology at my college, for four years of outstanding technological achievement.
About my Quantitative Finance Experience :
1. SEBI-Registered Research Analyst Firm – Quant Research Intern
Worked on optimization algos and alpha validation using walk-forward methods, built a Newton-Raphson based implied volatility engine, and deployed dashboard tools for performance and risk metrics visualization.
2. Crypto Derivatives Exchange (Startup) – Quant Trading Intern
Developed and executed crypto strategies using in-house APIs, improved order execution with limit IOC orders.
3. Sports Stock Exchange (Startup) – Quant Research Intern
Built pricing and scoring models for fantasy players, applied z-score models for player valuation, and worked on risk models for inventory of the exchange.
4. WorldQuant (Brain Program) – Research Consultant
Ranked in the global top 1% in their International Quant Championship and Brain competitions. Built alphas with high Sharpe and fitness scores.
5. Corporate Project @ iRageCapital Advisory (HFT firm)
Collaborated with one of the founding members of iRageCapital, on a strategy validation project using BankNifty options data. Integrated tools like Zipline, Pyfolio, and Cerebro for backtesting pipelines. We evaluated returns, drawdowns, and capital efficiency to professionalize the research outcomes.
Projects (not big ones though):
Transformer Based Hangman Solver -
Built a custom encoder-only transformer model that uses substring pattern analysis to predict masked words. Trained the model on over 100M+ synthetic samples using GPU acceleration and achieved ~60% accuracy.
Pairs Trading with Kalman Filters -
Worked on identifying cointegrated stock pairs and implemented a dynamic hedge ratio using Kalman filtering. Built a simple backtester that triggered trades based on half-life-based z-score thresholds.
Options Pricing Web App -
Created a small web app to price European options using Black-Scholes, Binomial Tree, and Monte Carlo simulations. Integrated visualizations of option Greeks and sensitivity analysis to observe how prices respond to input changes.
Competitions :
Over the last few years, I’ve actively participated in industry relevant competitions and research challenges.
Gold @ Inter-IIT Tech Meet 13.0 by Dream11 (Problem Statement: Built an AI-assisted fantasy team recommender using player data and GenAI-based interfaces)
Silver @ Inter-IIT Tech Meet 12.0 by Zelta Labs (Problem Statement: Algorithmic Trading Strategy Development for BTCUSDT spot crypto market)
Gold @ Inter-IIT Tech Meet 11.0 by QuantInsti (Problem Statement: Efficacy of Price Action Trading Strategies in context of the Indian Equities Market)
National Finalist @ American Express Campus Challenge - Decision Science Track(Problem Statement: Use boosting algorithms to predict T20 match outcomes using historical data)
[Inter-IIT Tech Meet: Premier intercollegiate technology competition among all the IITs in India. It spans weeks of structured problem-solving with real sponsors, like Adobe, Dream11, Zeltalabs, Quantinsti, Ideaforge, ISRO, etc... with their respective domains they are into.]
Leadership Experience :
Since my sophomore year, I’ve been part of the Quant Club at my college, eventually serving as its Governor. We hosted workshops, sessions and competitions — helping thousands of students explore quant finance and get hands on exposure.
* Advanced Calculus course outline:
Differential Calculus: Lagrange’s mean value theorem, Cauchy’s mean value theorem, Taylor’s and Maclaurin’s theorem. Functions of several variables: Limit, continuity, partial derivatives and their geometrical interpretation, total differential and differentiability, derivatives of composite and implicit functions, derivatives of higher-order and their commutativity, Euler’s theorem on homogeneous functions, Taylor’s expansion of functions, maxima and minima, constrained maxima/minima problems using Lagrange’s method of multipliers.
Differential Equations: First order exact differential equations, general linear differential equations with constants coefficients, method of variation of parameters, Cauchy-Euler equations. Power series solutions of ODE’s.
Integral Calculus: Improper integrals and tests for convergence, Beta and Gamma functions and their elementary properties. Differentiation under integral sign including variables limits-Leibnitz rule. Double and triple integrals, changing the order of integration, change of variables - Jacobian of a transformation, computation of surface area and volume.
Vector Calculus: Definition of vector and scalar fields, level surfaces, limit, continuity, differentiability of vector functions. Directional derivative, gradient, curl, divergence and their geometrical interpretation. Line integral, path independence of line integrals, Green’s theorem, surface integrals, Gauss divergence theorem, Stokes theorem.
** Linear Algebra, Numerical & Complex Analysis course outline:
Course Outline:
Linear Algebra: Vector spaces over an arbitrary field, subspaces, linear combination, spanning set, linear dependence and independence of vectors, basis and the dimension of vector spaces. The rank of a matrix and, solution of a system of equations using the rank concept, Gauss elimination method to solve a system of linear equations. Linear transformation, rank-nullity theorem, matrix representation of a linear transformation, Eigenvalues and Eigenvectors of matrices and their properties (Hermitian, Skew-Hermitian, Unitary matrices), diagonalization, Cayley-Hamilton Theorem(statement only).
Numerical Analysis: Iterative method for the solution of a system of linear equations Jacobi and Gauss-Seidel method. Solution for transcendental equations: Bisection, Fixed point iteration, Newton-Raphson methods. Finite differences, Interpolation, error in interpolation polynomial Newton’s forward and backward interpolation formula, Lagrange’s interpolation formula, Trapezoidal and Simpson’s 1/3 rules for numerical integration.
Complex Analysis: Limit, continuity, differentiability and analyticity of functions, Cauchy-Riemann equations, line integrals in the complex plane, Cauchy’s integral formula, derivatives of analytic functions, Cauchy’s integral theorem, Taylor’s series, Laurent series, zeros and singularities, residue theorem, evaluation of real integrals.
I’d love to get your input on :
1. Where I currently stand as a candidate
2. What realistic target schools should be on my radar
3. What more I can do before this upcoming application cycle
4. Based on the math courses I’ve completed (outlined above), do I meet the typical prerequisites expected by MFE programs? or would you recommend I take additional online courses?
Any guidance would be greatly appreciated. Thanks in advance!
I’m a recent undergraduate student from one of the old top 5 IITs in India, with a B.Tech from Mechanical Engineering Department. While my major wasn’t directly related to finance, I’ve spent the last 3 years building toward a career in quantitative finance and trading - through internships, real world quant research, and competitions. I’ve been meaning to apply for a MFE since last year. But to be honest, I didn’t feel confident that my profile was competitive enough during the last application cycle (December '24). So instead of rushing, I decided to focus on building further credibility.
Details about my Academic Performance :
CGPA: 7.64/10 (~3.27/4 scale)
While not stellar, my GPA reflects a split between my disinterest in the mechanical engineering core and my deepening focus on quantitative finance. I prioritized internships, competitions, and hands-on learning. This trade-off cost me academically.
Relevant math coursework:
Advanced Calculus* – 10/10
Linear Algebra, Numerical & Complex Analysis** – 10/10
Probability & Statistics – 6/10 (This was the one weak spot, caused by a health issue at the time. If needed, I’m open to retaking a formal course in this area to strengthen my academic narrative.)
Certifications :
I cleared CFA Level 1
I’ve registered for FRM Part 1 (August 2025 attempt)
I plan to take the GRE, TOEFL, and IELTS right after that
I will also be enrolling in the C++ Programming for Financial Engineering course offered by QuantNet and Baruch College
Awards: I was also honored with the Institute Order of Merit in Technology at my college, for four years of outstanding technological achievement.
About my Quantitative Finance Experience :
1. SEBI-Registered Research Analyst Firm – Quant Research Intern
Worked on optimization algos and alpha validation using walk-forward methods, built a Newton-Raphson based implied volatility engine, and deployed dashboard tools for performance and risk metrics visualization.
2. Crypto Derivatives Exchange (Startup) – Quant Trading Intern
Developed and executed crypto strategies using in-house APIs, improved order execution with limit IOC orders.
3. Sports Stock Exchange (Startup) – Quant Research Intern
Built pricing and scoring models for fantasy players, applied z-score models for player valuation, and worked on risk models for inventory of the exchange.
4. WorldQuant (Brain Program) – Research Consultant
Ranked in the global top 1% in their International Quant Championship and Brain competitions. Built alphas with high Sharpe and fitness scores.
5. Corporate Project @ iRageCapital Advisory (HFT firm)
Collaborated with one of the founding members of iRageCapital, on a strategy validation project using BankNifty options data. Integrated tools like Zipline, Pyfolio, and Cerebro for backtesting pipelines. We evaluated returns, drawdowns, and capital efficiency to professionalize the research outcomes.
Projects (not big ones though):
Transformer Based Hangman Solver -
Built a custom encoder-only transformer model that uses substring pattern analysis to predict masked words. Trained the model on over 100M+ synthetic samples using GPU acceleration and achieved ~60% accuracy.
Pairs Trading with Kalman Filters -
Worked on identifying cointegrated stock pairs and implemented a dynamic hedge ratio using Kalman filtering. Built a simple backtester that triggered trades based on half-life-based z-score thresholds.
Options Pricing Web App -
Created a small web app to price European options using Black-Scholes, Binomial Tree, and Monte Carlo simulations. Integrated visualizations of option Greeks and sensitivity analysis to observe how prices respond to input changes.
Competitions :
Over the last few years, I’ve actively participated in industry relevant competitions and research challenges.
Gold @ Inter-IIT Tech Meet 13.0 by Dream11 (Problem Statement: Built an AI-assisted fantasy team recommender using player data and GenAI-based interfaces)
Silver @ Inter-IIT Tech Meet 12.0 by Zelta Labs (Problem Statement: Algorithmic Trading Strategy Development for BTCUSDT spot crypto market)
Gold @ Inter-IIT Tech Meet 11.0 by QuantInsti (Problem Statement: Efficacy of Price Action Trading Strategies in context of the Indian Equities Market)
National Finalist @ American Express Campus Challenge - Decision Science Track(Problem Statement: Use boosting algorithms to predict T20 match outcomes using historical data)
[Inter-IIT Tech Meet: Premier intercollegiate technology competition among all the IITs in India. It spans weeks of structured problem-solving with real sponsors, like Adobe, Dream11, Zeltalabs, Quantinsti, Ideaforge, ISRO, etc... with their respective domains they are into.]
Leadership Experience :
Since my sophomore year, I’ve been part of the Quant Club at my college, eventually serving as its Governor. We hosted workshops, sessions and competitions — helping thousands of students explore quant finance and get hands on exposure.
* Advanced Calculus course outline:
Differential Calculus: Lagrange’s mean value theorem, Cauchy’s mean value theorem, Taylor’s and Maclaurin’s theorem. Functions of several variables: Limit, continuity, partial derivatives and their geometrical interpretation, total differential and differentiability, derivatives of composite and implicit functions, derivatives of higher-order and their commutativity, Euler’s theorem on homogeneous functions, Taylor’s expansion of functions, maxima and minima, constrained maxima/minima problems using Lagrange’s method of multipliers.
Differential Equations: First order exact differential equations, general linear differential equations with constants coefficients, method of variation of parameters, Cauchy-Euler equations. Power series solutions of ODE’s.
Integral Calculus: Improper integrals and tests for convergence, Beta and Gamma functions and their elementary properties. Differentiation under integral sign including variables limits-Leibnitz rule. Double and triple integrals, changing the order of integration, change of variables - Jacobian of a transformation, computation of surface area and volume.
Vector Calculus: Definition of vector and scalar fields, level surfaces, limit, continuity, differentiability of vector functions. Directional derivative, gradient, curl, divergence and their geometrical interpretation. Line integral, path independence of line integrals, Green’s theorem, surface integrals, Gauss divergence theorem, Stokes theorem.
** Linear Algebra, Numerical & Complex Analysis course outline:
Course Outline:
Linear Algebra: Vector spaces over an arbitrary field, subspaces, linear combination, spanning set, linear dependence and independence of vectors, basis and the dimension of vector spaces. The rank of a matrix and, solution of a system of equations using the rank concept, Gauss elimination method to solve a system of linear equations. Linear transformation, rank-nullity theorem, matrix representation of a linear transformation, Eigenvalues and Eigenvectors of matrices and their properties (Hermitian, Skew-Hermitian, Unitary matrices), diagonalization, Cayley-Hamilton Theorem(statement only).
Numerical Analysis: Iterative method for the solution of a system of linear equations Jacobi and Gauss-Seidel method. Solution for transcendental equations: Bisection, Fixed point iteration, Newton-Raphson methods. Finite differences, Interpolation, error in interpolation polynomial Newton’s forward and backward interpolation formula, Lagrange’s interpolation formula, Trapezoidal and Simpson’s 1/3 rules for numerical integration.
Complex Analysis: Limit, continuity, differentiability and analyticity of functions, Cauchy-Riemann equations, line integrals in the complex plane, Cauchy’s integral formula, derivatives of analytic functions, Cauchy’s integral theorem, Taylor’s series, Laurent series, zeros and singularities, residue theorem, evaluation of real integrals.
I’d love to get your input on :
1. Where I currently stand as a candidate
2. What realistic target schools should be on my radar
3. What more I can do before this upcoming application cycle
4. Based on the math courses I’ve completed (outlined above), do I meet the typical prerequisites expected by MFE programs? or would you recommend I take additional online courses?
Any guidance would be greatly appreciated. Thanks in advance!
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