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Microstructure Quantitative Researcher role at Point72 – build systematic macro strategies focusing on market microstructure.
Led by a quantitative portfolio team, this senior position involves research, feature engineering, backtesting, and production for global macro markets.
About the Team
A well-established quantitative portfolio management team at Point72 is looking for an experienced quantitative professional to develop and trade systematic macro strategies, with a focus on market microstructure. The candidate will be given the resources and support to drive the build out and expansion of the quantitative macro business.
Role/Responsibilities
Perform rigorous and innovative research to develop systematic signals for global macro (futures, FX, etc.) markets, with a focus on market microstructure signals
Perform feature engineering with order book tick data at intraday to daily horizons
Perform feature combination using various modeling techniques ranging from linear to machine learning models
Participate in the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation
Help drive the growth of the investment process and research capabilities of the team
Work in a team of highly qualified and motivated individuals with access to a cutting-edge research and trading infrastructure and clean datasets
Assist in building, maintenance, and continual improvement of production and trading environments
Requirements
MS or PhD in physics, engineering, statistics, applied math, quantitative finance, or other quantitative fields with a strong foundation in statistics
4+ years of experience in quantitative research, building statistical models for intraday to daily trading, as part of a successful proprietary trading team with a track record
Knowledge of market microstructure for futures and/or FX
Prior experience with tick data based feature generation, modelling, and monetization
Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas
Collaborative mindset with strong independent research abilities
Commitment to the highest ethical standards