Simulation Data Scientist
New York City / San Francisco / Remote /
Gauntlet’s mission is to help make blockchain protocols and smart contracts safer and more trustworthy for users. Building decentralized systems creates new challenges for protocol developers, smart contract developers, and asset holders that are not seen in traditional development and investing. Gauntlet is building a blockchain simulation and testing platform that leverages battle tested techniques from other industries to emulate interactions in crypto networks. Simulation provides transparency and greatly reduces the cost of experimentation so that teams can rapidly design, launch, and scale new decentralized systems.
Crypto systems are truly multi-disciplinary as they are at the intersection of distributed systems, cryptography, economics, and game theory. In particular, there is a coupling of economic incentives and product success, which results in a large attack surface that must be analyzed thoroughly. We are looking for people who are experts in their field and want to explore this exciting new space.
You will be working as part of an experienced team that has developed simulation software for many other industries, including high-frequency trading, autonomous vehicles and ride-sharing, and the natural sciences.
- Build agent-based simulations of smart contracts and blockchain networks using our Python SDK
- Design and optimize incentive models for blockchain protocols and help discover potential attack vectors
- Contribute to making our simulation model and platform world-class
- Build data models and visualizations of public blockchain data and simulation results that provide intuitive analytics to customers
- Proficient at writing code in Python or similar languages
- Experience with scientific computing packages such as Numpy/Scipy, Pandas, etc..
- Experience with distributed computation frameworks such as Spark, Flink, TensorFlow
- Smart contract development experience (e.g. Solidity)
- Experience with building machine learning models at scale