Quantitative Analyst - High Frequency Trading

About the company

The client is a quantitative investment firm focused on Indian financial markets. They operate a multi-strategy, multi-manager platform designed to generate consistent, risk- adjusted returns.

Their approach combines systematic investment methods, rigorous quantitative research and institutional-grade manager evaluation. We bring together research, technology and data to build scalable investment solutions.

Role Overview

We are seeking a Quantitative Developer with strong C++ and Python expertise to convert mathematical models and research prototypes into reliable, high-performance analytical engines.

You will work closely with Quantitative Research, Data Engineering, AI and Product Engineering teams throughout the full model lifecycle—from research handover and production implementation to validation, deployment and ongoing support.

This role is ideal for someone who enjoys working at the intersection of quantitative finance, numerical computing and production software engineering.

Key Responsibilities

Research Production

• Translate mathematical models and Python research prototypes into robust, production-quality C++.
• Develop reusable components for risk analytics, forecasting, portfolio analysis and simulation.
• Build efficient Python interfaces for C++ components using pybind11 or similar technologies.
• Ensure production implementations remain mathematically and numerically consistent with the underlying research.
• Establish clear and reproducible processes for transitioning models from research to production.

Engine Development and Validation

• Design and develop analytical engines capable of processing historical, batch and streaming data.
• Integrate calculation components with data pipelines, APIs, databases and downstream applications.
• Validate production implementations against research prototypes, benchmark datasets and expected results.
• Develop automated numerical, unit, integration, regression and performance tests.
• Identify and resolve numerical stability, precision and edge-case issues.
• Optimize calculation speed, memory usage, concurrency and scalability.
• Profile and benchmark critical components to meet defined performance requirements.

Deployment and Delivery

• Package analytical engines as libraries, services, APIs or containers.
• Support deployment across internal infrastructure and client-controlled environments.
• Configure engines for different datasets, workflows and institutional requirements.
• Assist with integration testing, production upgrades, issue diagnosis and technical troubleshooting.
• Implement appropriate logging, monitoring and error-handling capabilities.
• Document interfaces, assumptions, configurations, dependencies and deployment requirements.

Collaboration and Ownership

• Work closely with Quantitative Research, Data Engineering, AI and Product Engineering teams.
• Participate in technical design discussions, code reviews and quantitative model reviews.
• Communicate implementation trade-offs, constraints and risks clearly to technical and quantitative stakeholders.
• Take end-to-end ownership of assigned components, from research handover through production deployment and support.
• Contribute to engineering standards, reusable libraries and development best practices.

Required Qualifications

• Bachelor’s or master’s degree in Computer Science, Engineering, Mathematics, Statistics, Physics, Quantitative Finance or a related discipline.
• Strong professional programming experience in modern C++, including object- oriented and generic programming.
• Proficiency in Python and scientific-computing libraries such as NumPy, pandas or SciPy.
• Experience translating mathematical or analytical prototypes into production software.
• Strong understanding of algorithms, data structures, software architecture and design principles.
• Experience building automated unit, integration and performance tests.
• Familiarity with numerical methods, floating-point behaviour and numerical validation.
• Experience profiling and optimizing compute-intensive or data-intensive applications.
• Proficiency with Git and modern software-development practices.
• Strong analytical, debugging and problem-solving skills.
• •Ability to work effectively with both researchers and software engineers.

Preferred Qualifications

• Experience with pybind11, Boost.Python, Cython or similar interoperability technologies.
• Knowledge of quantitative finance, portfolio analytics, risk modelling, forecasting or simulation.
• Familiarity with time-series data and financial-market datasets.
• Experience developing applications that process batch or real-time streaming data.
• Exposure to concurrent, parallel or distributed computing.
• Experience with containerization and deployment technologies such as Docker.
• Familiarity with Linux environments, CI/CD pipelines and cloud or on-premises infrastructure.
• Experience building analytical libraries, calculation services or APIs for institutional users.
• Knowledge of Indian financial markets is advantageous