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Build and optimize quantitative trading signal infrastructure using Python, cloud tools, and ML libraries for a systematic investment firm.
10-week summer internship in New York with Point72's Market Intelligence (proprietary research) team: MS/PhD data scientists mine large, unstructured and alternative datasets using machine learning and statistical modeling to produce research insights for investment professionals. Core stack: Python, R, Spark, SQL.
A Summer 2027 software engineering internship at Point72's Cubist Systematic Strategies in New York. Interns build and maintain trading, research, and data-analysis applications for asset management, working with Python/Java/C++, Git/JIRA, and gaining exposure to AI, machine learning, big data, and cloud computing.
Research and develop ML models for financial markets, applying predictive modeling, NLP, and time-series analysis using Python/R/Matlab.
Build and optimize trading systems in Python/C++ while collaborating on a low-latency production platform for systematic trading.
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