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