Quantitative Researcher

Point72 Asset Management, L.P; London; On-site; GBP 60,000 - 80,000; Full time

Job description

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

Quantitative researcher to help build out a systematic macro (futures, FX, and vol) business. Core focus will be working on short-term to mid-frequency alpha strategies.

Responsibilities

• Develop systematic trading models across fixed income, currency and commodity (FICC) markets
• Manage the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation
• Perform feature engineering with price-volume, order book, and alternative data for intraday to daily horizons in mid frequency trading space
• Perform feature combination and monetization using various modeling techniques
• Assist in building, maintenance, and continual improvement of production and trading environments coupled with execution monitoring.
• Contribute to the research infrastructure of the team.

Requirements

• Background in mathematics, statistics, machine learning, computer science, engineering, quantitative finance, or economics
• 2-5 years of experience in macro quantitative trading, preferably FICC
• Experience synthesizing predictive signals for both cross-sectional and time-series models driven by statistical/technical, fundamental, and data driven signals
• Ability to efficiently format and manipulate large, raw data sources
• Strong experience with data exploration, dimension reduction, and feature engineering
• Demonstrated proficiency in Python. Familiarly with data science toolkits, such as scikit-learn, Pandas. Experience with machine learning is a plus
• 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