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GBM Equities Electronic Market Making Quant Researcher Associate New York

Goldman Sachs

New York, NYOn Site

Original Job Summary

About the Role

The GBM Equities Electronic Market Making Quant Researcher Associate New York role involves providing liquidity in US Equities and ETFs on-exchange and to low-touch client flows. The role combines quantitative research and low-latency trading infrastructure to develop and implement automated trading and risk management strategies.

Key Responsibilities

  • Research and model complex financial problems
  • Develop and implement automated trading strategies
  • Work with large datasets and timeseries analysis
  • Collaborate in a team focused on transparency and accountability

Basic Qualifications

  • Bachelor/Master’s degree with 2-3 years experience
  • Strong programming skills in Python and data science libraries
  • Ability to independently structure open-ended research questions
  • Experience with machine learning methodologies

Preferred Qualifications

  • Experience in large-scale collaborative codebases
  • Expertise in designing and back testing trading strategies
  • Experience with risk models and portfolio optimization

About Goldman Sachs

Founded in 1869 and headquartered in New York, Goldman Sachs is a leading global investment banking, securities and investment management firm committed to client success and community growth.

Benefits

Goldman Sachs provides competitive benefits and wellness offerings for eligible employees.

Key skills/competency

  • Quantitative Research
  • Python
  • Data Science
  • Trading Strategies
  • Risk Management
  • Liquidity
  • Machine Learning
  • Collaboration
  • Statistical Modeling
  • Market Analysis

How to Get Hired at Goldman Sachs

🎯 Tips for Getting Hired

  • Customize your resume: Tailor skills to quantitative research.
  • Highlight technical expertise: Emphasize Python and data skills.
  • Showcase collaboration: Provide teamwork examples.
  • Prepare research examples: Detail projects and outcomes.

📝 Interview Preparation Advice

Technical Preparation

Review Python libraries for data analysis.
Practice algorithmic trading simulations.
Study machine learning techniques for finance.
Analyze large financial datasets practically.

Behavioral Questions

Explain a challenging project in research.
Describe handling team conflict examples.
Discuss your accountability in team settings.
Share times you solved open-ended problems.