Senior ML Quant Researcher for Crypto Market Making

Hytech; Singapore; On-site; SGD 180,000 - 290,000; Full time

Job summary

Hytech, a global management consulting and quantitative trading firm, is seeking a senior ML quant to join its trading-focused team in Singapore. You will design, research, and deploy time-series ML models for crypto derivatives and market-making, working closely with researchers, traders, and engineers to turn insights into production-ready systems.

You will build robust backtesting pipelines and feature-rich data pipelines from high-frequency feeds, while ensuring low latency and risk

Qualifications

• Advanced degree in a quantitative field is required.
• 3+ years building production ML models for trading.
• Strong time-series ML and feature engineering experience.
• Proficiency in Python and ML libraries (NumPy, Pandas, scikit-learn).
• Knowledge of crypto derivatives and market microstructure is a plus.

Responsibilities

• Design, prototype, and validate ML models for market microstructure and price dynamics.
• Build backtesting, walk-forward, and out-of-sample evaluation pipelines.
• Engineer features from high-frequency data (order books, funding rates, etc.).
• Collaborate with traders to translate hypotheses into ML frameworks.
• Deploy models into low-latency trading systems and monitor performance.

Skills

Time-series ML

Statistical modelling

Model deployment

Communication to non-ML team

Education

Master's or PhD in CS/Statistics/Quant field

Tools

Python

NumPy

Pandas

Scikit-learn

PyTorch

TensorFlow

Kafka

Redis streams

C++/Rust

Job description

Hytech, a global management consulting and quantitative trading firm, is seeking a senior ML quant to join its trading-focused team in Singapore. You will design, research, and deploy time-series ML models for crypto derivatives and market-making, working closely with researchers, traders, and engineers to turn insights into production-ready systems.

You will build robust backtesting pipelines and feature-rich data pipelines from high-frequency feeds, while ensuring low latency and risk