About the Role
GMX is building out its internal risk function and looking for a Risk & Quantitative Analyst to help manage and calibrate risk across its perpetuals protocol. You'll work directly with the Risk Lead to replace external risk vendors with in-house capabilities — covering parameter modeling, risk monitoring, new market assessments, and data tooling.
This is a high-impact role where your work directly shapes protocol safety and capital efficiency for one of the largest decentralized perpetuals exchanges.
What You'll Do
Parameter Calibration & Modeling
• Calibrate and model key protocol parameters: price impact curves, open interest (OI) caps, borrowing fees, funding rates, and position limits.
• Build and maintain quantitative models that balance risk exposure with capital efficiency.
Risk Monitoring
• Monitor real-time and historical risk metrics across markets.
• Analyze suspicious activity patterns and flag potential exploits or manipulation.
• Develop dashboards and alerts for ongoing protocol health.
New Markets & Protocol Changes
• Assess risk profiles for new asset listings and protocol upgrades.
• Provide quantitative recommendations on market parameters for launches.
• Evaluate the risk impact of governance proposals and protocol changes.
Data & Tooling
• Write and maintain scripts (Python and/or JavaScript/Node) for data extraction, transformation, and analysis.
• Work with on-chain data sources including DataStore contracts, subgraphs (Subsquid, Goldsky), and protocol APIs.
• Build internal tools and notebooks that improve the team's analytical workflow.
What We're Looking For
Required:
• Strong quantitative background — math, statistics, physics, engineering, or quantitative finance.
• Proficiency in data analysis and scripting with Python and/or JavaScript/Node.
• Experience in market risk, trading risk, or DeFi risk.
• Ability to communicate complex risk concepts clearly to both technical and non-technical stakeholders.
Preferred:
• Hands-on exposure to DeFi protocols, especially perpetuals or derivatives.
• Experience working with on-chain data (Ethereum, Arbitrum, Avalanche, or similar).
• Familiarity with subgraph indexing, blockchain RPCs, and smart contract data structures.
• Background in quantitative trading, market making, or financial engineering.