Junior Quantitative Researcher (AI)

TL;DR

Junior Quantitative Researcher (AI): Develop predictive signals and AI-driven workflows for quantitative trading with an accent on signal research, root cause analysis, and market microstructure. Focus on designing AI agent infrastructure, automating research pipelines, and productionizing strategies across asset classes.

Location: Hong Kong (work-from-home arrangement may vary depending on business team needs)

Company

Leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users.

What you will do

• Develop, test, and productionize predictive signals using statistical methods, machine learning, and AI agent-driven workflows.
• Conduct root cause analysis on model behavior, signal decay, PnL attribution, and trading outcomes, building diagnostic tools.
• Research market microstructure including order book dynamics, execution costs, liquidity, and venue behavior.
• Design and extend AI agent infrastructure for automating research pipelines like data exploration and backtest configuration.
• Collaborate with traders, engineers, and researchers to deploy live, monitored strategies.

Requirements

• PhD (recent or near completion) in quantitative field: Computer Science, Machine Learning, Statistics, Physics, Mathematics, Electrical Engineering, Operations Research, or related.
• Strong Python programming; experience with NumPy, pandas, PyTorch or JAX.
• Hands-on experience building AI agents and LLM-based systems (tool-using agents, multi-step reasoning, retrieval, evaluation).
• Solid knowledge of statistics, probability, and machine learning.
• Genuine interest in financial markets and trading shown through projects, internships, or study.
• Strong written and verbal communication skills.

Nice to have

• Prior experience at hedge fund, prop trading firm, market maker, bank, or fintech.
• Knowledge of market microstructure, limit order books, or high-frequency data.
• Experience with backtesting, time-series analysis, or causal inference.
• Familiarity with low-latency systems or large-scale data infrastructure.
• Publications, open-source contributions, or trading competition results.

Culture & Benefits

• Work in a flat, user-centric global organization with world-class talent.
• Autonomy on fast-paced, innovative projects.
• Results-driven environment with career growth and learning opportunities.
• Competitive salary and company benefits.
• Work-from-home arrangement (may vary by team).