About Point72
Harnessing data for smarter investment decisions
Key Highlights
- Manages over $20 billion in assets
- Founded by billionaire investor Steven A. Cohen
- Employs around 2,000 professionals
- Headquartered in Stamford, Connecticut
Point72 Asset Management, LP, headquartered in Stamford, Connecticut, is a prominent hedge fund managing over $20 billion in assets. Founded by billionaire investor Steven A. Cohen, Point72 employs around 2,000 professionals and utilizes advanced data analytics and AI/ML technologies to drive invest...
🎁 Benefits
Point72 offers competitive salaries, performance-based bonuses, equity participation, generous PTO policies, and comprehensive health benefits. The fi...
🌟 Culture
Point72 fosters a culture of intellectual curiosity and data-driven decision-making. The firm emphasizes collaboration and innovation, encouraging emp...
Skills & Technologies
Overview
Point72 is hiring a Quantitative Researcher - Macro to develop systematic trading models across various asset classes. You'll work with Python, SQL, and machine learning techniques to generate alpha ideas and optimize portfolios. This role requires a strong quantitative background and experience in trading.
Job Description
Who you are
You hold a Master's or PhD in mathematics, statistics, physics, or a related quantitative discipline — your academic background equips you with the analytical skills necessary for rigorous research in financial markets. You have experience in quantitative trading, ideally focusing on FX or futures, and understand the intricacies of market anomalies and data-driven strategies.
Your expertise includes alpha research, portfolio construction, and optimization — you've built statistical and technical signals that drive investment decisions. You are comfortable with data exploration, dimension reduction, and feature engineering, allowing you to synthesize predictive signals for both cross-sectional and time-series models.
You possess strong analytical skills and a thorough understanding of financial markets — your ability to evaluate new datasets for alpha potential sets you apart. You are detail-oriented and maintain professionalism and integrity at all times, ensuring that your contributions positively impact the investment process.
Desirable
A PhD in statistics or machine learning is a plus, as is experience with mid-frequency alpha strategies. Familiarity with systematic trading models across FX, commodities, fixed income, and equity markets will enhance your ability to contribute effectively to the team.
What you'll do
As a Quantitative Researcher, you will develop systematic trading models that span various asset classes, including FX, commodities, fixed income, and equities. Your core focus will be on generating alpha ideas, backtesting strategies, and implementing them in a production environment. You will assist in building and maintaining the trading infrastructure, ensuring that it is robust and continually improving.
You will evaluate new datasets for their alpha potential, leveraging your analytical skills to identify opportunities for enhancement in existing strategies. Execution monitoring will be a key part of your role, as you will ensure that trading strategies are performing as expected and make adjustments as necessary.
Your contributions will be vital in growing the investment process and enhancing the research infrastructure of the team. You will collaborate closely with other researchers and traders, sharing insights and strategies to drive success in a competitive market.
What we offer
Point72 provides a dynamic work environment where you can thrive as a Quantitative Researcher. You will be part of a team that values innovation and analytical rigor, with access to a wealth of data and resources to support your research efforts. The annual base salary for this role ranges from $150,000 to $200,000, complemented by discretionary bonuses and a comprehensive benefits package. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds.
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