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.
Originally posted on