Quant Research Associate

  • Hong Kong
  • Negotiable
  • Permanent
  • Discipline:
  • Ref: 51062

We are looking for a Quant Research Associate to join our research team and work on quantitative strategies for crypto and DeFi markets.
You will work directly with researchers and founders on problems involving:

Market making

Delta-neutral strategies

Reinforcement learning

Portfolio allocation

Backtesting infrastructure

Market microstructure research
Responsibilities

Conduct quantitative research on crypto and DeFi markets

Analyze market data and identify alpha opportunities

Develop and evaluate trading strategies

Build and improve backtesting frameworks

Research applications of machine learning and reinforcement learning in trading

Design experiments and evaluate strategy performance

Collaborate with the team on production research initiatives
Requirements

PhD in Mathematics, Statistics, Computer Science, Physics, Financial Engineering, Quantitative Finance, or a related field

Strong mathematical and statistical background

Proficiency in Python

Experience with machine learning frameworks such as PyTorch or Scikit-Learn

Familiarity with quantitative research methodologies

Ability to work independently in a research-driven environment

Good written and spoken English
Preferred Qualifications

Previous internship experience at a quantitative trading firm, hedge fund, market maker, or research lab

Experience with reinforcement learning

Knowledge of financial markets or market microstructure

Experience working with order book or trade-level data

Interest in digital assets, crypto markets, or DeFi
What We Offer

Direct exposure to real-world quantitative research

Opportunity to work on live strategies and production research

Remote-first environment

Apply for this job

We are an inclusive organisation and actively promote equality of opportunity for all with the right mix of talent, skills, and potential. We welcome all applications from a wide range of candidates. Selection for roles will be based on individual merit alone.

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