Data Science Jobs in Web3

OpenSea is the first and largest marketplace for non-fungible tokens, or NFTs. Applications for NFTs include collectibles, gaming items, domain names, digital art, and many other items backed by a blockchain. OpenSea is an open, inclusive web3 platform, where individuals can come to explore NFTs and connect with each other to purchase and sell NFTs. At OpenSea, we're excited about building a platform that supports a brand new economy based on true digital ownership and are proud to be recognized as Y Combinator's #4 ranked top private company.

When hiring candidates, we look for signals that a candidate will thrive in our culture, where we default to trust, embrace feedback, grow rapidly, and love our work. We also know how critical it is to celebrate and support our differences. Employing a team rich in diverse thoughts, experiences and opinions enables our employees, our product and our community to flourish. We are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. To help facilitate this, we support remote, hybrid or onsite work at either New York City, San Francisco or the Silicon Valley for the majority of our opportunities.

OpenSea is looking for entrepreneurial Data Scientists to be founding members of our Marketplace Analytics team. They will work closely with the Marketplace Product & Engineering team to build a best-in-class system to optimize orders, match them, and offer them in a way that helps our buyers, creators, and sellers all achieve their goals while maintaining strong customer outcomes.

The ideal candidate will be deeply passionate about data & crypto and have a desire to deploy pioneering solutions against large scale problems. We are seeking candidates who are excited about wearing multiple hats and working closely with engineering and product to bring real solutions to market on fast timeframes, rapidly iterating from imperfect MVP through experimentation and development.