GBM Equities Electronic Market Making Quant Res...
@ Goldman Sachs

New York, NY
$187,500
On Site
Full Time
Posted 4 days ago

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Job Details

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
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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.

Frequently Asked Questions