Quantitative Researcher

Unchain Data; Singapore; Hybrid; SGD 120,000 - 240,000; Full time

Job summary

Unchain Data seeks a Quantitative Researcher to join a small team owning strategies end-to-end—from data and idea to live performance. You will research, design and implement market-making and systematic trading strategies, and deploy them with rigorous backtesting and simulation.

You will analyze market microstructure, build predictive models from diverse data sources, and monitor live strategies with traders and engineers. Proficiency in Python; Rust preferred; C++/Java useful.

Qualifications

• Degree in a quantitative or technical discipline (e.g. mathematics, physics, statistics, computer science, engineering).
• 0–5 years of experience in quantitative research or market making; exceptional new grads considered.
• Strong understanding of markets: order-book dynamics, risk, and funding.
• Proficiency in Python for research; Rust preferred; C++/Java useful.

Responsibilities

• Research and design market-making and systematic trading strategies from prototype to live trading.
• Analyze market microstructure across venues and adapt strategies accordingly.
• Build predictive models from order-book, trade and on-chain data using statistical methods.
• Backtest and validate models with rigorous simulations.
• Maintain research infrastructure: simulation, backtesting and data pipelines in Python and Rust.

Skills

Quantitative research

Market understanding

Backtesting

Python for research

Statistical modeling

Education

Bachelor's degree in mathematics/physics/statistics/CS/engineering

Advanced degree welcomed

Tools

Python

Rust

C++

Java

Job description

About The Position

We are 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 is growing 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 backtesting 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, backtesting and data pipelines — in Python and Rust.
• Evaluate new venues, instruments, protocols and data sources as candidates for new strategies.

Requirements

• 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-trade review — 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 backtest 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).