Quantitative Researcher

HKDAG (Singapore) Pte. Ltd.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

HKDAG (Singapore) Pte. Ltd. seeks a Quantitative Researcher to own strategies end-to-end—from idea and data through implementation and live trading—in a compact, high-responsibility team.

You will design market-making and systematic trading models, analyze market microstructure across venues, and build research infrastructure in Python and Rust, collaborating closely with traders and engineers to iterate quickly.

Qualifications

• Degree in a quantitative or technical discipline; advanced degree welcome but not required.
• 0–5 years of experience in quantitative research or market making; full-cycle experience preferred.
• Strong understanding of markets, order-book dynamics, and risk.

Responsibilities

• Research, design and implement market-making and systematic trading strategies; take from prototype to live trading.
• Analyze market microstructure across venues and adapt strategies accordingly.
• Build predictive models from order-book, trade, funding and on-chain data using statistical and ML methods; validate with backtesting.
• Monitor live strategies; review parameters and P&L; iterate with traders and engineers.
• Contribute to research infrastructure, simulation, back testing and data pipelines in Python and Rust.
• Evaluate new venues, instruments and data sources for new strategies.

Skills

Python

Rust

C++

Java

Statistics

Machine learning

Education

Bachelor's degree in a quantitative or technical discipline

Tools

Git

SQL

Job description

About the Position

Weare looking for a Quantitative Researcher to join a small team that owns strategies end to end: from idea and data, through implementation and deployment, to live performance. The set of markets and strategies we run isgrowing quickly, and you will be trusted with real responsibility early.

What You'll Do

• Research, design and implement market-making and systematic trading strategies, and take them from prototype to live trading.
• Analyze market microstructure across venues with very different fee, latency, funding and settlement mechanics, and adapt strategies accordingly.
• Build predictive models from order-book, trade, funding and on-chain data using statistical and machine-learning methods, and validate them with rigorous back testing and simulation.
• Monitor and improve live strategies: review parameters, diagnose P&L and inventory behavior, and iterate quickly with traders and engineers.
• Contribute to research infrastructure simulation, back testing and data pipelines in Python and Rust.
• Evaluate new venues, instruments, protocols and data sources as candidates for new strategies.

About You

• Degree in a quantitative or technical discipline(e.g. mathematics, physics, statistics, computer science, engineering);advanced degree welcome but not required.
• 0–5 years of experience in quantitative research, systematic trading or market making, in crypto or traditional markets. We strongly prefer candidates who have taken at least one strategy through the full cycle — research, implementation, live trading and post-tradereview — and will also consider exceptional new graduates.
• A real understanding of how markets work —order-book dynamics, adverse selection, inventory and funding risk — and the judgment to tell a sound idea from a back test artifact.
• Strong Python for research and data analysis. Rust is strongly preferred; experience with C++ or Java is useful.
• Comfortable with large, noisy, irregularly sampled datasets and with the statistics needed to draw careful conclusions from them.
• Disciplined about risk, monitoring and documentation when several strategies are running at once.
• Curious, self-directed and precise; comfortable with ambiguity and rapid iteration, and communicates clearly with both researchers and engineers.

Nice to Have

• Hands-on experience in liquidity provision or execution, in CeFi or DeFi (e.g. centralized perpetual venues, AMMs, on-chain order books).
• Exposure to derivatives beyond linear perpetuals, or to funding-rate and basis strategies.
• Familiarity with tokenized real-world assets, or with traditional exchange microstructure that transfers to them.
• Production Rust experience, or experience porting research code into a low-latency trading system.
• Working knowledge of exchange APIs, real-time market-data systems and on-chain data.
• Track record in competitive quantitative challenges (e.g. Kaggle, ICPC, trading competitions, mathematical olympiads).