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Ripple is seeking a Staff Data Scientist to serve as the technical lead for analytics across the company's diverse product and business portfolio. This is a high-impact individual contributor role focused on defining analytics vision, building reusable scientific frameworks, and leveraging AI to accelerate insights across the organization.
Key responsibilities include:
- Lead data science strategy and methodology standards across multiple product teams (Treasury, Markets, Custody, XRPL)
- Partner with product and business leaders to frame critical questions, set analytical rigor standards, and ensure decisions are grounded in consistent, thorough analysis
- Design and build reusable analytics frameworks at scale: product/network health metrics, causal inference playbooks, liquidity and adoption models, forecasting approaches
- Pioneer AI-accelerated analytics using LLMs and agentic workflows to automate routine analysis, scale insight generation, and enable self-serve exploration for non-technical partners
- Drive evidence-based evaluation of growth across customers, corridors, and on-chain activity, identifying causal drivers of adoption and volume
- Define and communicate key metrics that leadership operates on, translating complex technical results into clear narratives for executives and external stakeholders
- Mentor and raise the bar for the data science function through thought leadership and coaching across embedded teams
Required qualifications:
- 8+ years in data science or quantitative analysis with demonstrated senior-level impact across multiple teams
- Technical leadership experience influencing roadmaps and strategy at executive and execution levels
- Proven track record designing reusable analytics and measurement frameworks that scale
- Hands-on experience applying AI to analytics workflows (agentic analysis, AI-assisted insight generation, natural-language interfaces)
- Deep expertise in experimentation, causal inference, forecasting, and statistical modeling in product environments
- Strong programming skills in Python or R, SQL fluency, and experience with large-scale data infrastructure (Databricks, Airflow, dBT preferred)
- Exceptional communication skills to translate technical depth into executive narratives
- Advanced degree (MS, PhD) in quantitative field preferred
- FinTech, payments, crypto, or blockchain data experience is a strong plus
