Quantitative Researcher

Douro Labs·Europe - Remote·remote global
finance:systematicquant-researchIC4🧑🏽‍💻 Engineering
Compensation
Not disclosed
You will own the architecture of market infrastructure at its most fundamental level: designing the methodologies and data products that power institutional-grade pricing and indexing. This is not purely a backend role. You are equally a subject matter expert who will stand in front of sophisticated institutional clients and defend complex financial engineering in both technical and business terms. You will solve real problems: How do we construct a defensible world price for an asset? What data sources matter? How do we synthesize them into a methodology that traders, exchanges, and market makers can trust? And how do we explain that to centralized and decentralized exchange executives? Location: Europe Remote-first Language: English (Fluent) ABOUT OUR TEAM AND YOUR ROLE We are a well-rounded team: half of us are tech whizzes, while the other half excel in building partnerships with institutional clients, prime brokers, and the global financial infrastructure community. Communication is key to our network-driven approach. Remote Work: Our team is spread across the globe, from the US and South America to Europe and Asia. We are a digital-first organization where remote work is the norm. Startup-level Speed: We thrive in the dynamic DeFi space and love adaptable problem solvers who are eager to meet the evolving needs of the market. We're building an Amsterdam hub with strong technical and business development talent. The Mission: You will be architecting methodologies that define the future of market data. Your work will power transparent, defensible pricing across asset classes that have never had institutional-grade infrastructure, designed for sophisticated buyers who require the highest fidelity, lowest latency, and most reliable market signals. YOUR RESPONSIBILITIES - Design and develop data methodologies that synthesize multiple sources into defensible market signals. - Develop a deep understanding of available datasets, their limitations, and optim