GBM - Systematic Credit - Quantitative Engineering - VP - Bengaluru

GBM - Systematic Credit - Quantitative Engineering - VP - Bengaluru

30 Jul
|
Goldman Sachs
|
Bengaluru

30 Jul

Goldman Sachs

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 Recent 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 pipeline—from 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 the Vice 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
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. 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 (., TRACE, dealer runs, electronic communication network feeds) to establish a robust

📌 GBM - Systematic Credit - Quantitative Engineering - VP - Bengaluru
🏢 Goldman Sachs
📍 Bengaluru

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