Develop, backtest, and execute systematic ETF trading strategies across global markets; analyze market data, manage execution and risk, and collaborate with researchers to improve performance.
About the Role: We are seeking a highly skilled and motivated Systematic ETF Trader to join our dynamic trading team. The ideal candidate will be responsible for developing, testing, and executing systematic trading strategies focused on Exchange-Traded Funds (ETFs) across various asset classes. This is an excellent opportunity for a quantitative professional with a passion for financial markets and algorithmic trading.
Key Responsibilities:
- Strategy Development: Design, develop, and implement algorithmic trading strategies that trade ETFs across global markets.
- Data Analysis & Modeling: Utilize historical and real-time market data to identify inefficiencies and create predictive models that inform trading decisions.
- Backtesting & Optimization: Conduct rigorous backtesting of trading strategies to ensure robustness and optimize for risk-adjusted returns.
- Execution: Manage and execute trades through automated systems, ensuring minimal slippage, transaction costs, and market impact.
- Risk Management: Monitor and manage trading risks, including market risk, liquidity risk, and operational risk, ensuring compliance with risk guidelines and limits.
- Performance Analysis: Track and analyze the performance of strategies, identify areas for improvement, and implement iterative changes to enhance profitability and performance.
- Collaboration: Work closely with quantitative researchers, data scientists, and other traders to refine strategies and improve performance.
- Technology Integration: Leverage advanced tools and platforms for data analysis, strategy development, and execution (e.g., Python, SQL, MATLAB, etc.).
Requirements:
- Education: Bachelor's degree in Finance, Economics, Engineering, Computer Science, Mathematics, or a related field.
- Experience: Proven experience (2+ years) as a quantitative trader, systematic trader, or similar role, with a strong focus on ETFs and algorithmic trading.
- Technical Skills: Proficiency in programming languages such as Python, C++, Java, or similar, along with experience in financial modeling, data analysis, and backtesting.
- Quantitative Skills: Strong background in statistics, econometrics, or machine learning techniques, with the ability to apply them to trading strategies.
- Market Knowledge: Deep understanding of financial markets, especially ETFs, including structure, liquidity, and market microstructure.
- Problem-Solving: Strong analytical and problem-solving skills with a keen ability to think critically and adapt to evolving market conditions.
- Communication: Strong verbal and written communication skills for collaborating across teams and presenting findings to stakeholders.
The expected base salary for this New York, New York, United States-based position is $150,000-$300,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.
Goldman Sachs New York, New York, USA Office
200 West Street, New York, NY, United States, 10282
Goldman Sachs Edison, New Jersey, USA Office
Edison, United States
Goldman Sachs Jersey City, New Jersey, USA Office
Jersey City, United States
Goldman Sachs New York, New York, USA Office
New York, United States
Goldman Sachs Newark, New Jersey, USA Office
Newark, United States
Similar Jobs
Machine Learning • Payments • Security • Software • Financial Services
Provides enterprise technology support through phone, ticketing, and potentially chat channels. Troubleshoots hardware, software, network, access, and application issues; manages incidents and escalations; documents resolutions in ServiceNow or similar ITSM tools; analyzes trends and operations data; supports knowledge management, training, process improvement, and automation; and coaches junior analysts while maintaining customer service, security, and confidentiality standards.
Top Skills:
Call Center TechnologiesHardware InfrastructureItsmNetworkingServicenow
Machine Learning • Payments • Security • Software • Financial Services
Leads the design, automation, deployment, monitoring, and security of Azure cloud infrastructure. Builds Infrastructure as Code with Terraform, Bicep, or ARM templates; manages CI/CD pipelines through Azure DevOps and GitHub; and implements monitoring, logging, alerting, incident response, patching, and vulnerability management. Collaborates with engineering, development, architecture, security, and incident management teams to improve system reliability, scalability, performance, and cost efficiency.
Top Skills:
Arm TemplatesAzureAzure DevopsAzure MonitorBashBicepCi/CdElastic StackElk StackGithub ActionsGithub EnterpriseInfrastructure As CodeKubernetesPowershellTerraform
Machine Learning • Payments • Security • Software • Financial Services
Provide high-volume, phone-based technical support and first-line resolution for hardware, software, networking and access issues. Log and track incidents in ServiceNow/ITSM, escalate complex problems, suggest process improvements, and maintain security and confidentiality while working from an approved remote workspace on a set evening/weekend schedule.
Top Skills:
Call Center TechnologiesItsmPhone SystemsServicenowTicketing Systems
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
