GBM - Systematic Credit - Quantitative Engineering - VP - Bengaluru
Goldman Sachs · Posted Nov 6
Investment banking, securities, asset management, and wealth management services
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About the role
The Systematic Credit Team is a global, multi-disciplinary market-making group that uses quantitative methods, technology, and market insights to trade corporate bonds, credit derivatives, and Fixed Income ETFs, with the Bengaluru team working alongside desks in New York, London, and Hong Kong. The group designs, backtests, and deploys systematic market-making strategies across fragmented OTC credit markets. As a Quantitative Researcher at the Associate or Vice President level, the role owns the end-to-end quantitative pipeline—from sourcing and structuring complex credit datasets to engineering predictive features, building alpha models, and developing shared research and backtesting platforms.
What you will do
- 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 (e.g., TRACE, dealer runs, electronic communication network feeds) to establish a robust "Golden Source" for quantitative research.
- 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.
- 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.
Skills used in this role
What the employer is looking for
- Education: Master’s or PhD degree in a highly quantitative STEM discipline (e.g., Mathematics, Physics, Computer Science, Statistics, Operations Research, or Financial Engineering).
- 6+ years of experience with a proven track record of developing systematic trading strategies or advanced quantitative models (ideally within Fixed Income, Credit, or Macro).
- 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 mindset—combining 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.
Benefits and support
- Compensation and benefits are detailed in the job posting
About Goldman Sachs
The Goldman Sachs Group, Inc. is a leading global financial institution that delivers a broad range of financial services across investment banking, securities, investment management, and consumer banking to a diverse client base. Established in 1869 and headquartered in New York City, the firm serves corporations, financial institutions, governments, and high-net-worth individuals worldwide.
- Industry
- Investment Banking
- Company size
- 47400+ employees
- Founded
- 1869
- Location
- New York, New York, USA
- Funding stage
- Public Company
Leadership
Chairman and Chief Executive Officer
President and Chief Operating Officer
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