Staff Data Scientist
Staff Data Scientist serving as technical lead across Ripple's product and business analytics — defining metrics frameworks, causal inference playbooks, and forecasting models, and using AI/LLM tooling to scale insight generation. Core stack includes Python or R, SQL, and large-scale data tooling like Databricks and Airflow.
- Compensation
- $212K–$265K / yr
- Location
- San Francisco, CA, United States
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Full time
- Experience
- 8+ years
- Category
- Data science
- Posted
Skills
Must have
- 8+ years in data science or quantitative analysis with senior-level impact across multiple teams
- Technical leadership across cross-functional teams, influencing roadmaps and strategy at executive and execution levels
- Proven experience designing reusable analytics and measurement frameworks that scale across products
- Hands-on experience applying AI to accelerate analytics workflows (agentic analysis, AI-assisted insight generation, natural-language data interfaces)
- Deep expertise in experimentation, causal inference, forecasting, and statistical modeling in a product environment
- Expertise in Python or R and fluency in SQL
- Exceptional communication skills — able to turn technical depth into executive-ready narratives
Nice to have
- Experience with large-scale data tech (Databricks, Airflow, dbT)
- Experience with FinTech, payments, crypto, or blockchain data
- Advanced degree (MS, PhD) in a quantitative field
Full description
At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.
If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.
At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.
If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.
THE WORK
We're looking for a Staff Data Scientist to be the technical lead across Ripple's diverse product and business portfolio. You'll define the analytics vision, build the scientific frameworks the org uses to evaluate product and business performance, and use AI tooling to accelerate the speed and reach of analytics across the company.
In this role, you'll partner with product and business leads to frame the most important questions, set the analytical bar, and ensure decisions across surfaces rest on a consistent, thorough foundation. You'll operate as a force multiplier -- solving the hardest problems, building the frameworks others reuse, and leveling up data scientists across teams.
WHAT YOU'LL DO
- Be the DS tech lead across multiple product and business teams: setting methodological standards and unblocking the hardest analytical problems across areas including Payments, Stablecoin & Custody products.
- Partner with product and business leads to define analytics strategy: shaping which initiatives to invest in, what success looks like, and how we'll know.
- Build the scientific frameworks Ripple reuses at scale: product and network health metrics, causal inference playbooks, liquidity and adoption models, and forecasting approaches that work across institutional and developer surfaces.
- Pioneer AI-accelerated analytics: applying LLMs and agentic workflows to scale insight generation, automate routine analysis, and enable self-serve exploration for non-DS partners.