Description

Role Overview

Our client is a premier global quantitative trading firm deploying systematic, computer-driven trading strategies across liquid global asset classes. Driven by an intellectually rigorous, tech-forward, and collaborative culture, the firm leverages vast datasets and advanced technology to uncover market anomalies and capture alpha.

In this role, you will lead the end-to-end research cycle for systematic trading strategies-from alpha discovery and signal generation to production implementation and live monitoring. You will analyze large, multi-timeframe datasets using advanced statistical methods and machine learning techniques to discover market inefficiencies, refine trading signals, and build predictive models for production environments.

Key Responsibilities

1. Alpha & Signal Discovery: Conduct mathematical and statistical analysis on market, technical, and alternative datasets to identify predictive signals and systematic opportunities.
2. Conduct end-to-end research pipeline, including hypothesis testing, feature engineering, model training, back-testing, portfolio construction, and risk management.
3. Implement signal logic, features, and datasets into the firm's execution platform
4. Evaluate live model performance, signal behavior, and monetization efficiency over time, continuously optimizing models for changing market conditions.

Requirements & Qualifications

1. Advanced degree (Master's or Ph.D. preferred) in Mathematics, Statistics, Computer Science, Physics, Engineering, or a related quantitative field.
2. 2-8+ years of direct experience in quantitative research, systematic trading, or high-dimensional data science/machine learning.
3. Strong proficiency in Python (including scientific toolkits like NumPy, Pandas, SciPy) or equivalent languages (C++, R, C#).
4. Solid foundation in applied statistics, linear algebra, time-series analysis, and predictive modeling techniques.
5. Demonstrated experience processing, cleaning, and extracting signals from large, complex, multi-timeframe datasets.
6. Strong intellectual curiosity and a drive to solve complex, open-ended mathematical problems.
7. Ability to work with high autonomy while collaborating seamlessly in a global, team-oriented setting.

Preferred Experience

1. Proven track record in developing and deploying successful systematic strategies in production environments.
2. Familiarity with advanced machine learning, NLP, or modern AI techniques applied to quantitative finance.
3. Experience working with non-traditional or alternative financial datasets.

If this outstanding opportunity sounds like your next career move, please submit through "Apply Now" or send your resume in Word format to Matt Chung a tresume@pinpointasia.com and putQuantitative Researcher - Global Systematic Trading Firm - J11672 in the subject header.

Data provided is for recruitment purposes only.

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