Staff Data Scientist — AI-Driven Analytics Leader (Equity)

Ripple; San Francisco (CA); On-site; USD 212,000 - 265,000; Full time

Benefits offered by this job

Equity

Professional development budget

In-office collaboration

Team offsites

Job summary

Ripple is seeking a Staff Data Scientist to be the technical lead across its diverse product and business portfolio in California. You’ll define analytics vision, build frameworks, and use AI tooling to accelerate analytics across the company.

You’ll partner with product and business leads to frame questions, set analytical bar, and ensure decisions rest on solid data. You’ll mentor teams and raise the data science standard across surfaces.

Qualifications

• 8+ years in data science with senior impact across teams.
• Technical leadership across cross-functional teams influencing roadmaps.
• Reusable analytics frameworks and measurement systems for scale.
• Hands-on AI to accelerate analytics workflows (agentic analysis, AI insights).
• Expertise in experimentation, causal inference, forecasting and modeling.

Responsibilities

• Be the DS tech lead across multiple product and business teams.
• Define analytics strategy with product and business leads.
• Build scalable scientific frameworks used across surfaces.
• Pioneer AI-accelerated analytics to scale insights.
• Lead evidence-based growth evaluation and metric leadership.
• Mentor embedded teams and raise DS function standards.

Skills

Technical leadership

Cross-functional collaboration

Data science

Experimentation & causal inference

AI tooling

Communication

Education

MS or PhD in quantitative field

Tools

Databricks

Airflow

dbt

Job description

Ripple is seeking a Staff Data Scientist to be the technical lead across its diverse product and business portfolio in California. You’ll define analytics vision, build frameworks, and use AI tooling to accelerate analytics across the company.

You’ll partner with product and business leads to frame questions, set analytical bar, and ensure decisions rest on solid data. You’ll mentor teams and raise the data science standard across surfaces.