Staff Data Scientist — AI-Driven Analytics Leader

P2P; United States; Hybrid; USD 180,000 - 280,000; Full time

Benefits offered by this job

Bonuses

Equity

Health benefits

Retirement plan

Parental leave

Catered lunches

Job summary

Ripple is seeking a Staff Data Scientist to lead analytics across its product and business portfolio. You will define frameworks, set analytics standards, and apply AI tooling to accelerate insights across the company.

You will collaborate with product and business leads to frame questions, shape initiatives, and build scalable measurement systems, driving growth and responsible decision-making with robust data science practices.

Qualifications

• 8+ years of data science or quantitative analysis with senior impact across multiple teams.
• Proven technical leadership across cross-functional teams influencing roadmaps and strategy.
• Experience designing reusable analytics and measurement frameworks that scale across products.
• Hands-on AI-enabled analytics, agentic analysis, and NL data interfaces.

Responsibilities

• Be the DS tech lead across multiple product and business teams, setting standards and unblocking analytic problems.
• Partner with product/business leads to define analytics strategy and success metrics.
• Build scalable scientific frameworks for product and network health, forecasting, and causal inference.
• Pioneer AI-accelerated analytics to scale insight generation and automate routine analysis.
• Drive evidence-based evaluation of growth across customers, corridors, and on-chain activity.
• Define metrics leadership uses and translate results into executive narratives.

Skills

DS leadership

Analytics strategy

Experimentation

Python/R

SQL

Education

MS/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 frameworks, set analytics standards, and apply AI tooling to accelerate insights across the company.

You will collaborate with product and business leads to frame questions, shape initiatives, and build scalable measurement systems, driving growth and responsible decision-making with robust data science practices.