Quantitative Researcher PhD Graduate

Company Description

Republic Technologies (CSE: DOCT) (FSE: 7FM0) (OTCQB: DOCKF) is Canada’s first publicly listed Ethereum infrastructure and treasury company. We believe Ethereum is the foundation of future finance, and we are building the institutional backbone that will support tomorrow’s global capital markets. By holding ETH on our balance sheet and operating validator infrastructure, we generate sustainable revenue while strengthening and securing the Ethereum network for real-world, universal applications.

Role Description

This is a full-time, hybrid position (remote / New York City) for a Quantitative Researcher – PhD Graduate. The Quantitative Researcher will be responsible for developing and enhancing mathematical models that support the Company’s ETH acquisition strategies and yield-generation activities. The role involves conducting quantitative analyses of large-scale on-chain datasets, Ethereum network metrics, and market microstructure to identify statistically robust patterns and signals.

Key responsibilities include designing and implementing quantitative frameworks to evaluate validator performance, MEV exposure, validator efficiency, and capital allocation scenarios. The role also requires back-testing research hypotheses using historical blockchain and market data, and translating validated models into production-ready code.

The successful candidate will incorporate real-time and non-traditional data sources—such as consensus-layer metrics, execution-layer logs, L2 rollup activity, and gas market dynamics—into the research process. The researcher will collaborate closely with the engineering and treasury teams to integrate quantitative insights into operational systems and support data-driven decision-making across the organization.

Qualifications

PhD in Mathematics, Statistics, Physics, Computer Science, Engineering, or another highly quantitative discipline.

Strong foundation in probability, statistics, machine learning, and time-series analysis; familiarity with optimization and stochastic processes.

Demonstrated ability to work with large, complex datasets; prior experience with on-chain data or distributed systems is a plus.

Proficiency in translating mathematical models into code (Python preferred; Rust, C++, or R is additive).

Independent research experience, with a track record of producing rigorous, reproducible work.

Comfort working in a fast-paced, high-accountability environment with multiple stakeholders.

Strong written and verbal communication skills, with the ability to present complex findings clearly.