About the position
Goldman Sachs Asset Management manages approximately $3.2 trillion across a variety of asset classes and mandates including institutional portfolios, high net worth accounts and mutual funds, using sophisticated investment models that have been developed in an innovative research environment. Current investment solutions span all geographies and include actively managed quantitative and fundamental strategies across all asset classes, fund and index replication, global tactical asset allocation, tax advantaged offerings, income based strategies, factor based investing, and ESG oriented portfolios - with more strategies under development. As one of the longest-running teams in the industry, GSAM has developed a strong reputation for innovation, excellence, teamwork and camaraderie. Quantitative Investment Strategies or QIS, manages over $100 billion across a variety of mandates including institutional portfolios and funds. We employ a mix of sophisticated quantitative models, that have been developed in an innovative research environment to help our clients achieve better returns. As one of the longest-running quantitative teams in the industry, QIS has developed a strong reputation for innovation, excellence and teamwork.
Responsibilities
- Design, build and maintain scalable, high throughput, cloud native systems for quantitative research and trading.
- Collaborate with quants, traders, and data scientists to develop customized workflows for model generation, portfolio rebalancing and trade execution.
- Optimize system performance to reduce latency, in a high availability, high-throughput trading environment.
- Ensure code quality, security and compliance in a highly regulated financial environment.
- Mentor junior engineers and contribute to shaping development and architectural best practices.
Requirements
- Strong expertise in Java and Python.
- Knowledge of scripting languages (e.g. bash, ksh, PERL).
- Deep understanding of cloud platforms (AWS and/or GCP) and experience with K8s, Docker and Terraform.
- Experience in serverless architectures, distributed computing and microservices.
- Experience working with machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn etc).
- Experience in Object Oriented Analysis, Design and testing best practices.
- Strong data structures, algorithms, and distributed computing concepts.
- Strong working experience with database technologies - Schema & no-schema - Sybase, DB2, Mongo, Single Store.
- Experience with all stages of the development lifecycle: inception, analysis, design, review, testing, and deployment.
- Experience with CI/CD pipelines.
Nice-to-haves
- Prior experience with global financial markets and quantitative investment and trading approaches.
- Knowledge of market data, portfolio management systems, order management systems.
- Understanding of quantitative finance and portfolio optimization.
