GBM - Systematic Credit - Quantitative Engineering - Associate - Bengaluru

Goldman Sachs · Bengaluru · 3+ yrs experience · Posted 2026-07-07

Tech stack: C++, Java, Python, SQL

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About the role

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 (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.
Required Experience & Education
Education: Master’s 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 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.
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 (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.
- Required Experience & Education
- Education: Master’s 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 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.
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.

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.

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 (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.
- Required Experience & Education
- Education: Master’s 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 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.

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