Senior Quantitative Researcher
We’re working with a Global Crypto focused Hedge Fund looking to onboard a crypto-focused Senior Quantitative Researcher in their NYC office to design, implement, and run systematic trading strategies across digital asset markets. You’ll own the full lifecycle: from research and model prototyping to execution, risk management, and post-trade analytics while collaborating with engineering to enhance infrastructure and data pipelines. The ideal candidate pairs rigorous quantitative skills with practical market intuition in 24/7, high-volatility environments. Role & Responsibilities: Research, design, and deploy alpha signals and market-making/arbitrage strategies across centralized and decentralized crypto venues. Build, backtest, and optimize models using high-frequency market data; drive continuous improvement through robust experiment design and performance attribution. Own production trading: monitor strategies, manage parameters, and respond to market conditions with disciplined risk controls. Partner with engineering to enhance low-latency execution, data quality, and simulation frameworks; contribute to tooling and automation. Implement portfolio construction, transaction cost modeling, and risk management (liquidity, slippage, tail risk, venue/operational risk). Produce clear, data-driven reporting on PnL drivers, risk exposures, and strategy health. Requirements: Advanced quantitative background (e.g., MS/PhD in Math, CS, Statistics, Physics, EE) or equivalent industry experience in systematic / quantitative trading. Strong programming proficiency (Python preferred; C++/Rust/Go a plus) and fluency with data structures, numerical methods, and distributed computing. Proven track record building and running strategies in crypto or HFT/market-making (tick-level data, microstructure, latency-aware execution) as a Quantitative Trader. Deep understanding of crypto market structure (CEX/DEX, funding rates, basis, liquidity fragmentation, gas/MEV, custody/operational considerations). Expertise in statistics and ML for time series (signal discovery, feature engineering, regime detection, cross-validation, overfitting controls). Rigorous risk mindset and production discipline: monitoring, alerting, rollback plans, and thorough documentation. Compensation & Benefits: Competitive base salary ($200k to $250k) and PnL tied bonus structure Comprehensive benefits package including health coverage and retirement plans.