Senior Staff Data Scientist — AI-Driven Analytics Leader

Ripple; San Francisco (CA); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Bonuses

Equity

Job summary

Ripple is seeking a Staff Data Scientist to lead analytics across its product and business portfolio. You will define analytics vision, build scalable frameworks, and drive AI-powered insights to scale analytics across the company.

You'll collaborate with product and business leads to frame questions, set analytic standards, and ensure decisions rest on solid data foundations. This role demands advanced modeling, experimentation, and leadership to elevate data science across teams.

Qualifications

• 8+ years of data science or quantitative analysis with multi-team impact.
• Technical leadership across cross-functional teams influencing roadmaps and strategy.
• Experience designing reusable analytics and measurement frameworks.

Responsibilities

• Be the DS tech lead across multiple product and business teams, setting standards.
• Partner with product and business leads to define analytics strategy and success metrics.
• Build scalable scientific frameworks for product and network metrics and forecasting.
• Pioneer AI-accelerated analytics using LLMs and agentic workflows.
• Drive evidence-based growth evaluation across customers and on-chain activity.
• Communicate metrics and insights to Ripple executives and stakeholders.
• Mentor and raise the DS function across teams.

Skills

Data science leadership

Cross-functional collaboration

AI in analytics

Statistical modeling

Python or R

SQL proficiency

Forecasting

Databricks

Education

MS or PhD in quantitative field

Tools

Databricks

Airflow

dbt

Job description

Ripple is seeking a Staff Data Scientist to lead analytics across its product and business portfolio. You will define analytics vision, build scalable frameworks, and drive AI-powered insights to scale analytics across the company.

You'll collaborate with product and business leads to frame questions, set analytic standards, and ensure decisions rest on solid data foundations. This role demands advanced modeling, experimentation, and leadership to elevate data science across teams.