GBM Systematic Credit Quantitative Engineering Associate Bengaluru

Team Overview

The Systematic Credit Team is a global multi-disciplinary market-making group that leverages advanced quantitative methods technology and deep market insights to trade corporate bonds credit derivatives and Fixed Income ETFs.

Operating at the intersection of financial engineering machine learning and high-performance computing our team in Bengaluru works in lockstep with global desks in New York London and Hong Kong. We design backtest and deploy systematic market-making strategies that provide liquidity capture alpha and manage risk in historically fragmented and over-the-counter (OTC) credit markets.

Your Impact

As a Quantitative Researcher at the Associate or Vice President level you will drive the research agenda and infrastructure for our systematic credit market-making strategies. You will take ownership of the end-to-end quantitative pipelinefrom sourcing and structuring complex credit datasets to engineering predictive features building alpha models and developing the core platforms that democratize signal generation across the broader team.

For candidates entering at theVice President (VP)level you will also be expected to lead key architectural decisions for our research platform mentor junior researchers and collaborate directly with global trading desks to transition models from research into production.

Key Responsibilities

1. Alpha Generation & Strategy Development:Conduct rigorous statistical research to identify predictive signals (alphas) across corporate bonds and credit ETFs. Apply advanced time-series analysis machine learning and alternative data processing to model credit spread dynamics.
2. Consolidated Research-Grade Data Framework & Pipeline:Architect and build a consolidated high-performance research-grade data framework and pipeline to back signal generation. Ingest clean and normalize diverse noisy credit datasets (e.g. TRACE dealer runs electronic communication network feeds) to establish a robust Golden Source for quantitative research.
3. AI-Driven Self-Service Signal Backtesting Platform:Design develop and maintain an open scalable AI-based platform that allows researchers and traders to seamlessly upload signal ideas leverage machine learning for automated parameter tuning and backtest them against a standardized point-in-time and bias-free simulation framework.
4. Quantitative Infrastructure & Tooling:Collaborate with quantitative developers to build and scale backtesting engines simulation frameworks and production-grade analytics libraries. Ensure research code is modular well-tested and optimized for high-performance computing environments.

Required Experience & Education

• Education:Masters or PhD degree in a highly quantitative STEM discipline (e.g. Mathematics Physics Computer Science Statistics Operations Research or Financial Engineering).

Experience:

• 3 years of professional experience in quantitative research financial engineering or data science.

Core Competencies & Technical Skills

• Quantitative & Fixed Income Foundations:Deep understanding of probability statistics linear algebra and time-series analysis paired with a strong conceptual grasp of bond pricing yield-to-price conversions credit spreads and interest rate risk (duration/convexity).
• Advanced Programming:Advanced proficiency in Python (Pandas NumPy SciPy Scikit-Learn) with a software engineering mindsetcombining rapid mathematical prototyping with clean modular and well-documented code. Object-oriented programming in C or Java is highly desirable.
• Data Engineering & Quantitative Toolkit:Experience managing large-scale noisy and unstructured datasets using SQL and high-performance time-series databases (e.g. KDB/Q ClickHouse). Proficient in applying machine learning techniques (regression tree-based models neural networks) to financial data.
• Intellectual Honesty & Collaborative Communication:Driven to understand market mechanics rather than just curve-fitting. Possesses the analytical honesty to challenge assumptions iterate on failed hypotheses and translate complex quantitative concepts into clear actionable insights for global stakeholders.

Preferred Qualifications

• Direct experience researching systematic corporate bond or credit derivatives strategies.
• Experience building self-service quantitative research platforms APIs or shared backtesting frameworks.
• Hands-on experience with KDB/Q or managing large-scale tick-level financial datasets.