Quantitative Researcher

Point72; Greater London; On-site; GBP 60,000 - 90,000; Full time

Job summary

Point72 is seeking a dedicated research professional focused on discovering systematic anomalies in global macro markets. The role involves conducting innovative research, managing an end-to-end research pipeline, and contributing to analytical frameworks.

Candidates should possess a strong background in quantitative fields, with 2-6 years of experience in macro trading. Proficiency in programming languages such as Python, R, or C/C++ is essential, along with a commitment to ethical standards.

Qualifications

• 2-6 years of signal research experience in macro trading.
• Prior professional experience with feature engineering or modeling.
• Demonstrated proficiency in Python, R, or C/C++.

Responsibilities

• Perform research to discover systematic anomalies in macro markets.
• Manage the research pipeline end-to-end.
• Contribute to scalable research analysis frameworks.

Skills

Python

R

C/C++

Statistics

Machine Learning

Feature Engineering

Data Manipulation

Education

Background in mathematics, statistics, computer science, or related field

Tools

scikit-learn

Pandas

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/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