Quantitative Researcher

Responsibilities

• Work on greenfield predictive modeling problems related to blockchain performance and system/user behavior
• Devise and prototype various approaches to solve open-ended problems
• Implement production-grade solutions and take end-to-end ownership of production prediction pipelines

Requirements

• 3+ years of experience building predictive models, preferably in HFT or traditional tech
• Excellent knowledge of predictive modeling techniques including linear regression, decision trees, and neural nets
• Proficiency in numpy and pandas
• Proven ability to build significant projects from scratch
• Creative, self-motivated, and able to work independently

Preferred Qualifications

• Crypto-native experience

Benefits

• Opportunity to tackle deeply complex and technically demanding problems with high autonomy
• Impactful work on a high-performance, parallel EVM Layer 1 blockchain
• Collaboration with a world-class, highly-motivated team
• Culture of low ego and high-quality output

About the Company

Monad Foundation is a team of dedicated ecosystem and community builders on a mission to massively grow the impact of decentralized tech. We are building Monad, a performant and parallel EVM Layer 1 designed to power global on-chain finance.