This role involves performing innovative research on market anomalies, managing data processing and modeling in mid frequency trading, and improving portfolio trading in production.
ABOUT CUBIST
Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.
ROLE/RESPONSIBILITIES
- Perform rigorous and innovative research to discover systematic anomalies in global macro markets (futures, FX, etc.)
- Perform feature engineering with price-volume, order book and alternative data at intraday to daily horizons in mid frequency trading space
- Perform feature combination and monetization using various modeling techniques
- Manage the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation
- Maintain and improve portfolio trading in a production environment
- Contribute to the analysis framework for scalable research
REQUIREMENTS
- Background in mathematics, statistics, machine learning, computer science, engineering, quantitative finance, or economics
- 2-6 years of signal research experience in macro trading as part of a trading team
- Specialization in swaps, fixed income, or commodities trading a plus.
- Prior professional experience with feature engineering, modeling, or monetization
- Ability to efficiently format and manipulate large, raw data sources
- Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas
- Strong command of foundations of applied and theoretical statistics, linear algebra, and machine learning techniques
- Collaborative mindset with strong independent research abilities
- Commitment to the highest ethical standards
Point72 London, England Office
8 St. James' Square, London, United Kingdom, SW1Y 4JU
Similar Jobs
Financial Services
Conduct original alpha research on global macro assets, building and testing predictive signals (trend timing and directional). Model dynamics across rates, FX, equities and commodities, evaluate signals statistically, contribute to portfolio construction and risk analysis, monitor live strategy performance, and collaborate with researchers and portfolio managers to implement and refine trading strategies.
Top Skills:
Python
Financial Services
Conduct original deep learning research to improve large-scale time-series and cross-sectional forecasting. Design, train and evaluate models, improve representations, objectives, optimisation and robustness to distribution shift, and work with engineers to productionize research for low-latency, high-throughput trading systems.
Top Skills:
Deep LearningGpusHpcLow-Latency SystemsOptimisationProbabilistic ModellingReinforcement LearningTime-Series Modeling
Fintech
Join AllianceBernstein's Systematic Fixed Income team to develop, backtest, and implement systematic fixed-income and credit strategies. Responsibilities include portfolio optimization, factor discovery and analysis, risk attribution, and contributing to a Python-based research platform (abAlphaLabs). Collaborate across the investment process and support ongoing management and enhancement of systematic strategies.
Top Skills:
PythonSQL
What you need to know about the London Tech Scene
London isn't just a hub for established businesses; it's also a nursery for innovation. Boasting one of the most recognized fintech ecosystems in Europe, attracting billions in investments each year, London's success has made it a go-to destination for startups looking to make their mark. Top U.K. companies like Hoptin, Moneybox and Marshmallow have already made the city their base — yet fintech is just the beginning. From healthtech to renewable energy to cybersecurity and beyond, the city's startups are breaking new ground across a range of industries.
