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As a Quantitative Research Engineer, design, develop, and implement software for automated trading systems using advanced statistical techniques. Collaborate with researchers and prioritize business needs.

As a PhD intern, you will design and develop software solutions for automated trading systems, collaborating closely with the Quantitative Research team.

Support trade lifecycle activities including clearance, settlement, and cash management. Collaborate with trading/financing desks, counterparties, and custodians. Identify and implement process and technology improvements, perform data analysis (Excel), and learn securities financing and prime brokerage workflows.

As a Quantitative Researcher Intern, you'll develop models and strategies, conduct statistical analysis, and implement algorithms in a trading context.

Develop high-performance research platforms and tools that bring trading strategies to life. Work in small teams to design, build, and deploy resilient systems using distributed computing, NLP, and machine learning to solve large-data problems in finance.

Support investment teams by acquiring, cleaning, and analyzing multi-source data to test investment theses, identify inflection points, and present research. Learn fundamental investor approaches, build decision-support tools, and communicate findings clearly to Sector Data Analysts and investment teams.

The Quantitative Researcher develops trading models and conducts research using complex statistical techniques, requiring a PhD in a quantitative field.

11-week internship building high-performance research platforms and trading tools. Work in small teams on distributed systems, NLP/ML applications, and web frameworks while collaborating with senior engineers.

Optimize and manage portfolio-level trading strategies by translating quantitative research into production. Tune allocators and risk, minimize trading costs and market impact, monitor performance in real time, and conduct statistical research to improve risk-adjusted returns.

Support investment teams by acquiring, cleaning, and analyzing diverse datasets to test investment theses. Learn fundamental investor approaches, identify key data drivers and inflection points, and present research findings. Work with existing and new data to generate insights and decision-support tools for sector-focused investment research.