Objective:

We are seeking a skilled Data Science contractor to develop a dynamic fee model for our automated market maker (AMM) in the crypto space. The primary goal of this project is to maximize yield for Liquidity Providers (LPs) by dynamically adjusting pool fees based on various market parameters.

Statement of Work:

The Data Science contractor will be responsible for the following tasks:

  1. Research and understand current AMM protocols such as Uniswap and Sushiswap, with a focus on their fee structures and yield optimization strategies.
  2. Investigate leading research in dynamic pool fees, including concepts like Loss vs. Rebalancing, mark-outs, and other relevant topics.
  3. Develop a comprehensive model for a dynamic fee structure, taking into account variables such as volatility, gas prices, trading volume, and other relevant factors.
  4. Configure model parameters and validate the model using historical data from protocols like Uniswap and Sushiswap.
  5. Create clear documentation and present findings to the company's stakeholders, including recommendations for implementing the dynamic fee model into the AMM.
  6. Collaborate with the company's development team to ensure a smooth integration of the dynamic fee model into the existing AMM infrastructure.

Job Requirements:

  1. Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field.
  2. Proven experience in building and implementing statistical models, preferably within the crypto or financial industry.
  3. Strong understanding of AMM protocols in the crypto space, including knowledge of their fee structures and yield optimization strategies.
  4. Proficiency in programming languages such as Python or R, and experience using data analysis tools like pandas, NumPy, and TensorFlow.
  5. Excellent research skills, with the ability to comprehend and synthesize complex concepts related to dynamic pool fees and other relevant topics.
  6. Strong communication and presentation skills, with the ability to effectively convey technical concepts to both technical and non-technical stakeholders.
  7. Self-motivated and able to work independently, while also collaborating effectively with cross-functional teams.