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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.

As a PhD Intern, you will develop mathematical models, conduct statistical analysis, and implement trading strategies while collaborating with senior researchers.

Conduct quantitative research for investment strategies using advanced statistical and mathematical modeling, machine learning, data analysis, portfolio optimization, and algorithm development. Build predictive models and analyze price formation, risk, and financial market drivers. Collaborate with researchers, engineers, and traders to develop scalable systems and solve real-world investment problems.

SREs at Citadel focus on optimizing and maintaining system reliability, performance, and automation for investment applications, collaborating closely with teams.

Collaborate with quantitative researchers to analyze data, develop research tools, and enhance and maintain automated trading systems and algorithms. Build scalable software solutions using quantitative techniques to address complex financial problems. The role requires strong programming, Linux development experience, creative problem-solving, business-value prioritization, and effective stakeholder communication.

Conduct ML research (deep learning, NLP, statistics) on large and unstructured datasets, implement algorithms in production-quality code, back-test models, and document findings to inform trading strategies.

Summer intern role building high-performance, large-data research platforms and web frameworks. Collaborate in small teams to create tools for trading strategies, applying distributed computing, NLP, and machine learning.

Develop mathematical models and trading strategies, back test trading models, conduct research and statistical analysis for monetization systems.

Conduct ML research (deep learning, NLP) on large and unstructured financial datasets, implement algorithms in high-quality code, back-test models, document findings, and collaborate with senior researchers during an 11-week summer internship in Hong Kong or Singapore.

Conduct quantitative investment research by building predictive models, applying statistical and mathematical techniques, optimizing multi-asset portfolios, and developing optimization algorithms. Analyze financial markets, price formation, and risk using large-scale data and coding in C++ or Python. Collaborate with researchers, engineers, and traders to develop scalable systems and identify investment opportunities.