Crypto Quant Trader (Arbitrage & Market Making)

Key Responsibilities
Conduct quantitative research on short-term inefficiencies, liquidity, and price dynamics within a single exchange

Design and evaluate arbitrage and market-making strategies, including:
Funding-rate–driven strategies Perpetual futures pricing opportunities Inventory-aware liquidity provision

Build and maintain event-driven backtests that realistically model execution, funding, latency, and partial fills.

Support live or paper-traded strategies through:

• Performance monitoring

• Post-trade analysis and P&L attribution 

• Execution quality and slippage analysis

• Identification of strategy and operational failure modes Iterate on strategies using feedback from live trading behavior and changing market conditions.

Required Qualifications

• Minimum 3 years of proven, relevant experience in this role is required.

• Strong proficiency in Python with experience contributing to research or production trading codebases. Optional/beneficial but not mandatory, working knowledge of at least one systems language such as C++ or Rust (or equivalent).

• Solid understanding of crypto market mechanics, including centralized exchange structures, perpetual futures, and funding rate dynamics.

• Practical experience with execution and risk management, including slippage, partial fills, and basic inventory risk.

• Strong quantitative foundation in probability, statistics, time-series analysis, and market microstructure. Prior experience working with high-frequency market data and supporting live or paper-traded strategies, including performance monitoring, post-trade analysis, and execution quality assessment.

• Ability to conduct independent quantitative research, take ownership of well-scoped projects, and collaborate closely with experienced developers and researchers.

What We Expect at This Level

• Can take a strategy from hypothesis → backtest → deployment support → post-trade review

• Understands the gap between backtest results and live performance

• Able to diagnose underperformance using execution and market data

• Comfortable working in a lean environment with high ownership and minimal supervision

Nice to Have

• Experience supporting or operating live trading strategies

• Familiarity with order- book-based or event-driven backtesting systems