Head of Quantitative Derivatives Research

Company:  Miracles Fintech

Location:

Ahmedabad, Gujarat, India
Department:  Quantitative Research

Employment Type:

Full-Time

About Miracles Fintech
Miracles Fintech is a technology-driven proprietary trading and research firm focused on building scalable, high-performance quantitative trading systems. We operate across Indian financial markets including NSE, BSE, and MCX, with a strong emphasis on scientific research, data-driven modeling, and systematic strategy development.
Our infrastructure includes direct exchange connectivity, tick-level data access, and tightly integrated research-to-production pipelines.

Role Overview
Miracles Fintech is seeking a  Head of Quantitative Derivatives Research  with a strong mathematical and modeling foundation to lead our derivatives research initiatives.
This role is centered on rigorous quantitative modeling of derivatives markets, with a focus on volatility dynamics, stochastic processes, and statistically robust alpha generation. The position is research-first in nature, emphasizing mathematical depth, empirical validation, and systematic implementation.
The successful candidate will build and lead a dedicated derivatives research team, define modeling standards, and develop production-grade systematic strategies grounded in quantitative theory.

Core Research Mandate
The role will focus on:
Developing mathematically rigorous models for derivatives pricing and forecasting.
Researching and modeling implied volatility surface dynamics across strikes and expiries.
Studying structural inefficiencies in index and stock derivatives markets.
Modeling:
Volatility clustering and persistence
Skew and smile dynamics
Term-structure evolution
Intraday volatility microstructure
Designing and implementing numerical pricing methods including:
Monte Carlo simulation
Finite difference methods
PDE-based solvers
Stochastic volatility models (Heston, SA , Local Volatility)
Building statistically robust backtesting systems capable of handling full option chains and tick-level data.
Translating theoretical research into systematic, production-grade derivatives strategies.

Key Responsibilities
Define and drive the firm's derivatives research roadmap.
Lead hypothesis-driven research using sound mathematical reasoning.
Establish best practices for:
Model calibration
Statistical validation
Robustness testing
Out-of-sample verification
Ensure all strategies are supported by rigorous empirical evidence.
Collaborate closely with technology teams to convert research models into efficient, scalable production systems.
Build, mentor, and lead a high-caliber quantitative research team.

Required Expertise
Mathematical & Quantitative Foundations
Strong foundation in probability theory and stochastic calculus.
Deep understanding of risk-neutral pricing frameworks.
Experience working with stochastic differential equations.
Advanced knowledge of time-series modeling and statistical inference.
Strong analytical and theoretical problem-solving ability.
Derivatives Modeling
Experience implementing and calibrating:
Black-Scholes and model extensions
Local volatility models
Stochastic volatility models
Jump-diffusion or regime-switching models
Deep understanding of implied volatility surfaces and Greeks from a mathematical perspective.
Familiarity with derivatives market microstructure.
Programming & Numerical Skills
Strong proficiency in Python (Num Py, Sci Py, Pandas).
Experience implementing numerical methods efficiently.
Ability to optimize large-scale computations.
C++ experience preferred for performance-critical modeling.
Experience working with large historical and tick-level datasets.

Research Philosophy
We value scientific rigor, mathematical depth, and disciplined empirical testing.
All strategies must be grounded in:
Sound quantitative theory
Statistically significant results
Robust validation methodologies
This role is ideal for researchers who enjoy deep mathematical modeling, structured problem-solving, and building systematic frameworks rather than discretionary trading.

Education
PhD or Master's or Bechlor' degree in:
Mathematics
Statistics
Physics
Quantitative Finance
Engineering (with strong mathematical background)
Strong academic grounding in stochastic processes and advanced probability theory preferred.

What Makes This Role Compelling
Direct access to exchange-level data and technology infrastructure.
Opportunity to architect a derivatives research function from the ground up.
Research-driven culture with strong emphasis on quantitative rigor.
Competitive compensation aligned with experience and research capability.
Long-term leadership opportunity within a growing quantitative trading firm.

Application
Interested candidates may apply to:
[HIDDEN TEXT]
Website: