Quantitative Researcher

Engineers Gate is seeking a researcher to join a small team focused on systematic intraday short term and medium term equity strategies. The researcher will collaborate with the Portfolio Manager to develop and backtest new models for intraday equity prediction, utilizing statistical learning techniques to analyze large and diverse datasets. The ideal candidate will have an advanced degree in Computer Science, Engineering, Applied Mathematics, or a related STEM field, as well as strong programming skills in Python and experience with version control and unit testing. The salary range for this role is anticipated to be between $125K and $150K.

• Develop and backtest new models for intraday equity prediction
• Apply sound financial intuition and statistical knowledge in model development
• Work closely with Portfolio Manager to optimize alpha research
• Utilize firm's proprietary technology and data platform to optimize research
• Contribute to systematic short term and medium term equity strategies with statistical edge
• Analyze data and identify predictive signals to generate superior investment returns

• Apply sound financial intuition and statistical knowledge to develop and backtest new models for intraday equity prediction
• Collaborate with other team members to explore, analyze, and extract value from a large number of datasets from the existing data platform
• Utilize statistical learning techniques to explore and analyze a large and diverse set of datasets
• Combine financial and statistical methods to identify predictive signals for generating superior investment returns
• Utilize existing distributed machine learning and analysis tools to do large-scale equity alpha research for predictive modeling and optimization
• Aid in the development of new tools to shorten the research loop
• Work closely with the Portfolio Manager and have exposure to an extensive and mature technology portfolio
• Have a focus on systematic intraday short term and medium term equity strategies that have a statistical edge without being latency-sensitive.