Senior Quant Developer

2 months ago

Mumbai, IndiaSenior / Staff+

Responsibilities

Lead the architectural design and extensibility patterns for Beacon’s Python pricing and risk libraries.
Own end-to-end delivery of pricing models for complex fixed-income, structured-credit, commodity, FX, and index products.
Define and enforce implementation, testing, validation, and coding standards for quantitative models.
Serve as the primary technical reviewer for model implementations, architecture proposals, and team code standards.
Architect and manage scalable AWS infrastructure supporting quantitative research and front-office production.
Build reliable end-of-day processing pipelines, automated regression testing, observability, and recovery capabilities.
Design client-specific configuration infrastructure while maintaining performance and maintainability.
Implement market-data pipelines integrating data from multiple market-data vendors.
Represent quantitative engineering expertise in client discussions, implementation scoping, and pre-sales engagements.
Mentor developers, conduct technical interviews, and contribute to hiring decisions.

8–12 years of experience in quantitative development or financial engineering with a strong production track record in pricing and risk systems.
Expert Python skills, including designing libraries, optimizing performance with NumPy vectorization, Cython, and multiprocessing, and setting coding standards.
Deep fixed-income pricing knowledge across rates derivatives, credit products, structured products, model calibration, and Greeks.
Strong commodities derivatives knowledge across energy, metals, or agricultural markets, including seasonality and multi-factor models.
Experience owning a quantitative model or library end-to-end in a production fintech or financial-services environment.
Experience with automated testing, production observability, and CI/CD pipelines.
Proven ability to lead and develop technical teams through mentoring and hiring participation.
Bachelor’s or postgraduate degree in a quantitative discipline such as mathematics, physics, engineering, computer science, or financial engineering.
Experience with stochastic-volatility models such as SABR, Heston, and LMM and their numerical implementation is preferred.
C++ experience, including shared pricing libraries consumed through Python wrappers such as pybind11 or ctypes, is preferred.
Familiarity with XVA frameworks, real-time risk pipelines, distributed job orchestration, real-time data ingestion, and distributed computing frameworks is preferred.

1,001-5,000 employees