Quantitative Researcher - Pricing Model

Role Overview We are looking for a Quantitative Analyst / Researcher to evaluate, test, and enhance our pricing models for plain vanilla perpetual swaps and options across crypto and equity tokens. You will stress-test model assumptions, evaluate risk under extreme market regimes, and build novel models from scratch. This role requires deep expertise in raw volatility pricing, proven model validation experience, and a first-principles approach to translating technical research into production-grade quantitative models. Key Responsibilities Model Development & Optimization Enhance Existing Models: Test, benchmark, and improve current pricing models for plain vanilla perps and options (crypto & equity tokens). Build from Scratch: Design and prototype novel mathematical models for new derivative instruments and tokenized structures. Test Core Assumptions: Deeply audit, challenge, and empirically test underlying model assumptions against live 24/7 market data. Model Risk & Validation Validation & Governance: Perform end-to-end model validation, identifying edge cases, structural limitations, and failure points. Risk & Stress Testing: Evaluate model performance, greeks (delta, gamma, vega), and liquidity exposure under extreme market scenarios and tail-risk events. Quantitative Research & Volatility Volatility Analytics: Calibrate and maintain raw volatility pricing, implied volatility surfaces, skew/smile dynamics, and funding rate models. Research Implementation: Read, critique, and implement cutting-edge technical/academic research papers to solve complex quantitative problems. Derivatives & Volatility Expertise: Hands-on experience with derivatives pricing (options, perps), raw volatility modeling, and surface calibration. Model Validation Background: Strong track record in model risk, backtesting, and stress testing within quantitative finance or trading environments. First-Principles Mindset: Ability to deconstruct crypto market mechanics from first principles rather than relying strictly on legacy TradFi assumptions. Research Capability: Ability to quickly digest and code complex formulas from technical research papers. Technical Skills: Advanced proficiency in Python (NumPy, SciPy, Pandas) or C++ for quantitative prototyping and analysis. Education: Master’s or Ph.D. in Financial Engineering, Quantitative Finance, Mathematics, Physics, or a related field. Domain Knowledge: Strong understanding of crypto market microstructure, funding rates, and tokenized equity/RWA assets. What We Offer Collaborative remote work environment that allows you to have a work life balance. Growth framework that drives fast, continuous improvement Opportunity to learn and collaborate with the leadership team. Exciting team offsites and employee engagement activities. Competitive compensation and exposure to closely with teams. #J-18808-Ljbffr