The Quantitative Researcher will collaborate with traders, engineers, and other stakeholders to drive profitable, data-informed trading strategies in both CeFi and DeFi markets. The primary focus is on signal research, trade analytics, and return attribution—with the ultimate goal of helping trading teams improve their performance and scale their businesses sustainably.
You will work on a range of strategies, including proprietary trading, market making on centralized and decentralized exchanges, and DeFi-focused arbitrage. Success in this role requires strong communication skills, analytical rigor, and the flexibility to adapt to rapidly evolving market conditions.
- Collaboration & Advising Stakeholder Management:
- Serve as a trusted partner for traders, operations, and business leaders to provide insights, identify areas for improvement, and align on strategic initiatives.
- Communication:
- Present analyses, explain quantitative methodologies, and guide stakeholders on interpreting results.
- Respond promptly to ad-hoc requests from trading teams for data-driven insights or performance diagnostics.
- Tools & Resource Utilization:
- Mentor traders and engineers on leveraging in-house analytics platforms, data visualization tools, and frameworks that streamline research and help them optimize trades.
- Process & Continuous ImprovementResearch Infrastructure:
- Collaborate with other Quantitative Researchers and engineers to build and refine data processing pipelines (both on-chain and traditional market data).
- Contribute to the development of internal libraries, backtesting frameworks, and risk analytics tools.
- Project Ownership:
- Lead or co-lead large-scale research initiatives that cut across multiple teams, ensuring timely delivery and stakeholder alignment.
- Proactively identify gaps in existing models or data coverage, prioritizing high-impact improvements.
The ideal candidate demonstrates expertise in quantitative analysis techniques including statistical analysis, has experience working with market data and large data sets programmatically, and strong analytical skills. Additionally, the candidate should have experience with financial data.
- Strong programming skills for quantitative analysis
- Proven Python skills – including working knowledge of analytical libraries and of complex codebases. Knowledge of at least another language is preferable.
- Excellent communication skills
- Interest in learning from other risk takers, using feedback to improve analysis and understanding of strategies
- Attention to detail and passion/ grit for getting things done, digging into problems, and helping develop solutions
- Sense of ownership and entrepreneurialism to grow the business
- Passion for investing / economics / stock market
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