- Location
- New York, New York, United States
- Workplace
- Onsite
- Type
- Full-time
- Department
- Data Science
- Seniority
- Senior
- Source
- Recruitee
Description
At Fun.xyz, we believe a tokenized future is a beneficial inevitability, granting financial emancipation to everyone with an internet connection. For a blockchain-first global economy to exist, value exchange must become intuitive, secure, and seamless.
Enter Checkout — the highest converting payment solution in web3. By integrating Checkout, dApps enable users to complete any on-chain action using crypto from a wallet, centralized exchange, or on-ramped fiat from a card or bank account. With access to market-leading routing and their asset portfolio at the point of sale, users are faster, stickier, and more engaged. For dApps, Checkout scales their user acquisition potential without launching new chains or integrating multiple third party providers.
We built Checkout because we envision a world where anyone, anywhere, can purchase anything with any asset. If that sounds like a vision you can get behind, we want to hear from you.
Role Overview
We’re looking for a Data Scientist to supercharge our data-driven strategy and ensure we’re building the right things at the right time. You’ll be embedded in the heart of our product and engineering teams, shaping insights that guide strategy, uncover user behavior, and optimize execution across Fun.xyz.
This role is perfect for someone who thrives in fast-paced, ownership-driven environments, loves solving open-ended problems, and is excited about the potential of blockchain data.
Responsibilities
Design and build scalable data models to analyze transaction flows and user behavior
Support the optimization of data transformations, pipelines, and workflows
Work directly with the CFO to provide insights on the business’ performance
Maintain data quality, observability, and reproducibility across analytical systems
Partner with engineers and product teams to deliver actionable, production-grade insights
Run experiments, apply causal inference, and drive evidence-based product decisions
Translate complex data into clear stories for both technical and non-technical stakeholders