Quantitative Researcher: Build Trading Signals (Python/C++)

NLS Executive Search; Hong Kong; On-site; HKD 1,200,000 - 2,000,000; Full time

Benefits offered by this job

Competitive base

Bonus potential

Job summary

NLS Executive Search is assisting a global trading firm in Hong Kong to hire a quantitative researcher. You will develop high- and mid-frequency trading signals using statistics and ML, test strategies on historical data, and build execution models to maximize risk-adjusted returns.

You will analyze market microstructure and diverse datasets, collaborating with developers and traders to move research into production in a fast-paced environment.

Qualifications

• Advanced degree in a quantitative field (Master's or Ph.D.).
• 3+ years of relevant experience.
• Strong coding ability in Python, C++, or R.
• Experience handling large, noisy financial or alternative datasets.
• Solid foundation in probability, statistics, and linear algebra.
• Clear verbal and written English skills to work in a fast-paced team environment.

Responsibilities

• Research and develop high-frequency or medium-frequency trading signals using statistical and machine learning methods.
• Test trading strategies on historical market data using robust simulation frameworks.
• Build execution and portfolio construction models to maximize risk-adjusted returns.
• Analyze market microstructure, order book dynamics, and alternative datasets to find new edges.
• Work closely with quantitative developers and traders to move research into live production.

Skills

Python

C++

R

Statistics

Machine learning

English fluency

Education

Master's or PhD in a quantitative field

Tools

Pandas

NumPy

SQL

Backtesting

Job description

NLS Executive Search is assisting a global trading firm in Hong Kong to hire a quantitative researcher. You will develop high- and mid-frequency trading signals using statistics and ML, test strategies on historical data, and build execution models to maximize risk-adjusted returns.

You will analyze market microstructure and diverse datasets, collaborating with developers and traders to move research into production in a fast-paced environment.