• Own the **full research cycle**: from defining research problems and choosing methods to synthesis, storytelling, and driving action • Proactively navigate **research constraints** (limited access, regulatory boundaries, technical complexity) and still deliver impact • Act as a **strategic research partner** for product and design teams, influencing product direction, not just validating solutions • Build strong, trust-based relationships with Product, Design, and other stakeholders • Translate complex qualitative and quantitative findings into **clear, actionable insights** • Actively contribute to and promote a **research-driven culture**, helping teams understand when and how to use research • Work confidently with ambiguity and complexity, be ready to learn about crypto and more

Role Description As a Senior Quantitative Researcher, you will own end-to-end research and production pipelines for one or more trading strategies. You'll lead research initiatives that generate alpha and improve execution quality, mentor junior researchers, and collaborate closely with our Trading desk to translate quantitative insights into profitable systematic strategies while maintaining rigorous risk management. Core Responsibilities - Own end-to-end research and production pipelines for a strategy - Lead alpha research initiatives leveraging advanced statistical and machine learning techniques - Process and analyze high-frequency tick data, order book snapshots, and market microstructure signals with sub-millisecond latency requirements - Analyze price formation, market liquidity dynamics, and limit order book imbalances across electronic venues - Build and run Monte Carlo simulations to estimate P&L distributions, risk exposures, and portfolio dynamics - Develop, backtest, and optimize quantitative trading strategies with rigorous statistical validation - Interpret complex model outputs and communicate alpha generation mechanisms to portfolio managers - Write modular, clean, and efficient Python code; build custom analytics libraries and research frameworks - Lead design reviews and establish data quality and research reproducibility standards - Guide 1–2 junior researchers through project delivery and model development - Proactively engage with traders and infrastructure teams to clarify research objectives and resolve data dependencies Execution & Market Microstructure - Design and implement transaction cost analysis (TCA) frameworks to measure and improve execution quality - Build market impact models and optimal execution strategies to minimize slippage across venue types - Develop real-time monitoring systems for latency, adverse selection, and execution performance metrics - Partner with infrastructure teams on ultra-low-latency data feeds and smart order routing algorithms Qualifications - Master's degree in a quantitative discipline (Mathematics, Statistics, Physics, Computer Science, Financial Engineering) strongly preferred; PhD a plus - Expert-level Python skills; able to build production-grade research and trading systems - Strong SQL skills; experience with complex queries on tick databases and time-series datasets - Deep experience with Monte Carlo methods, stochastic calculus, and probabilistic modeling - Proven ability to develop, backtest, and deploy systematic trading strategies with demonstrable P&L - Experience processing high-frequency tick data and real-time market feeds - Familiarity with AWS or similar cloud infrastructure for large-scale backtesting and research - Track record of mentoring junior quantitative researchers - Excellent communication skills; ability to present complex quantitative research to portfolio managers and trading desks - Deep understanding of equity/futures market microstructure, order types, and matching engine logic - Experience building sophisticated TCA frameworks with market impact decomposition - Familiarity with FPGA or kernel-bypass networking for latency-critical applications - Minimum of 5 years of experience in quantitative research, systematic trading, or statistical modeling Nice to Have - Proficiency in Rust, C++, or other systems languages for performance-critical components - Experience with MLOps, model monitoring, and adaptive retraining pipelines for regime detection - Background in derivatives pricing, options market making, or volatility arbitrage - Familiarity with FIX protocol, Betfair or Matchbook API experience, and ultra-low-latency trading infrastructure Benefits - Base salary: $165,000 - 200,000 + Bonus Plan

• Develop a comprehensive understanding of customers by crafting and leading mixed methods research • Promote the voice of the customer across the organization • Evaluate ambiguous and emerging use cases and product areas to define and prioritize research needs • Collaborate with leads and product teams to proactively plan roadmaps • Improve mixed-methods research practices

• Spearhead end-to-end research for "big bet" initiatives • Scale research capability by coaching Product Managers and Designers • Audit and re-engineer manual workflows using AI-driven automation tools • Architect and maintain a centralized repository for historical data • Establish "Rules of the Road" for AI-augmented research • Facilitate high-stakes post-research workshops