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