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Chaos Labs
DeFi Data Scientist
3 months ago | 339 views | 5 applications

DeFi Data Scientist

Full-time
Remote
Per year
$105,000 To $120,000

About the company

Chaos Labs is the first automated, on-chain economic security system enabling crypto protocols to optimize risk management and capital efficiency while protecting user funds. We use world-class custom EVM simulations to understand protocol mechanics and scenario implications. Leading protocols use our platform and tools, helping them manage and secure billions of dollars in assets, including Aave, GMX, dYdX, Venus, and more.

Job Summary

Key Responsibilities:

šŸ“Lead and conduct innovative research to analyze the behavior and interactions of diverse participants in the decentralized finance ecosystem, driving the development of advanced models within our DeFi simulation platform. šŸ“Communicate your findings and strategic recommendations to internal teams and key stakeholders in the DeFi space, with opportunities to publish research that influences industry standards. šŸ“Explore and develop new algorithms and techniques to uncover critical insights related to DeFi protocol security and optimization, enhancing the safety and efficiency of decentralized finance protocols. šŸ“Build sophisticated data models and visualizations using public blockchain data and simulation results, providing clear and actionable analytics to top DeFi protocols such as AAVE, Venus, and Radiant.

Requirements

šŸ“Requirements: šŸ“4+ years of professional experience in Data Science, with a strong focus on quantitative analysis and algorithm development. šŸ“At least 2 years of experience working within the DeFi or FinTech sectors, with a solid understanding of market dynamics in these areas. šŸ“Proven track record in model development, including production-grade implementation and ongoing performance monitoring. šŸ“Broad understanding of the crypto and blockchain ecosystem. šŸ“Experience with tools such as Dune or familiarity with writing smart contracts. šŸ“Proficiency in Python and relevant data science libraries, along with a solid understanding of machine learning models and techniques.

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