The Electronic Market Making Cash Trading team at Goldman Sachs needs a Quant Researcher to systematically provide liquidity in US Equities and ETFs, developing and implementing automated trading and risk management strategies using quantitative research and low-latency trading infrastructure.
Requirements
- Strong programming skills in Python and associated data science libraries (e.g. numpy, pandas, scikit-learn)
- Ability to independently structure and model open-ended research questions and drive results forward across end-to-end pipeline from research to production implementation
- Experience working with large datasets and timeseries analysis, as well as machine learning methodologies
- Experience contributing to large-scale collaborative codebases
- Experience in designing, modelling and back testing trading strategies
- Experience working with risk models and portfolio optimization
Responsibilities
- Leverages quantitative research combined with low-latency trading infrastructure to develop and implement automated trading and risk management strategies.
- Researching and modelling complex problems
- Structuring and modeling open-ended research questions
- Driving results forward across end-to-end pipeline from research to production implementation
- Working with large datasets and timeseries analysis
- Working with machine learning methodologies
- Designing, modelling and back testing trading strategies
Other
- Bachelor/Master’s degree with 2-3 years of experience in researching and modelling complex problems
- Thrives in collaborative setting where ideas are openly discussed and challenged
- Demands a high level of ownership and accountability from each contributor, while strongly emphasizing collaboration and transparency.
- Seeking a dedicated and ambitious researcher who thrives in a challenging environment and wants to make a meaningful impact at the firm.
