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.
Requirements
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.
Benefits
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.
Originally posted on Himalayas