Ripple
Ripple

22 Ripple Data Scientist Jobs Hiring Near You

GTreasury, now a Ripple solution, was acquired by Ripple in 2025, marking a significant expansion ... We are seeking an innovative and forward-thinking Staff Data Scientist and Engineer passionate ...

Staff Data Scientist

Chicago, IL · On-site

$196K - $245K/yr

At Ripple, we're building a world where value moves like information does today. It's big, it ... We are seeking an innovative and forward-thinking Staff Data Scientist and Engineer passionate ...

Software Engineer II, Data & AI

San Francisco, CA · On-site

$134.90K - $162K/yr

... within Ripple's central Data Engineering. This team implements the data ingestion and ... You work well across functions and teams, including data science, product, application engineering ...

At Ripple, we're building a world where value moves like information does today. It's big, it ... You work well across functions and teams, including data science, product, application engineering ...

At Ripple, we're building a world where value moves like information does today. It's big, it ... Bachelor's and/or Master's degree in Computer Science, Computer Engineering or related technical ...

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Ripple Jobs Information

What are the key skills and qualifications needed to thrive as a Data Scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks, data visualization tools, and big data platforms like TensorFlow, Tableau, and Hadoop, as well as certifications in data science, are highly valued. Excellent problem-solving skills, curiosity, and the ability to communicate complex findings clearly set outstanding data scientists apart. These skills and qualities are crucial for extracting actionable insights from data, driving business decisions, and collaborating effectively with stakeholders.

What are some typical projects Data Scientists work on, and how do they collaborate with other teams?

Data Scientists often work on projects such as building predictive models, analyzing large datasets to uncover trends, and developing data-driven solutions to business problems. They regularly collaborate with cross-functional teams, including software engineers, data engineers, and business analysts, to ensure that their insights are actionable and aligned with business goals. Effective communication and teamwork are essential, as Data Scientists frequently need to present complex findings to non-technical stakeholders and incorporate feedback from various departments.

What are Data Scientists?

Data Scientists are professionals who use statistical, analytical, and programming skills to collect, analyze, and interpret large volumes of data. They extract insights and trends from complex data sets to help organizations make data-driven decisions. Data Scientists often work with machine learning, data mining, and big data technologies to build predictive models and solve business problems. Their work bridges the gap between technical data analysis and actionable business strategy.

What is the job of a data scientist?

A data scientist analyzes large datasets to extract insights and support decision-making using statistical methods, programming, and data visualization tools. They often work with machine learning models and require skills in programming languages like Python or R, as well as knowledge of databases and data analysis techniques.

What is the difference between Data Scientist vs Data Analyst?

AspectData Scientist
Required CredentialsDegree in Computer Science, Statistics, or related field; often requires advanced degrees
Work EnvironmentResearch and development, predictive modeling, machine learning projects
Employer & Industry UsageTech companies, finance, healthcare, consulting firms
Common Search & ComparisonOften compared due to overlapping skills in data analysis and modeling

Data Scientists focus on building predictive models, advanced analytics, and machine learning, often requiring higher-level technical skills and education. Data Analysts primarily interpret existing data, generate reports, and support decision-making with descriptive analytics. While both roles analyze data, Data Scientists handle complex modeling and predictive tasks, whereas Data Analysts focus on data interpretation and reporting.

What is it like to work at Ripple?

Ripple is a technology company that values innovation, collaboration, and a customer-centric approach, fostering a dynamic and inclusive work environment.

The company's structure is divided into various teams, including engineering, product, and operations, which work together to develop and implement blockchain-based solutions for cross-border payments and financial inclusion. Ripple's headquarters is located in San Francisco, with additional offices in various locations worldwide, offering opportunities for remote work and collaboration.

Working at Ripple may appeal to candidates who are passionate about fintech, blockchain technology, and making a positive impact on the global financial system, as the company offers a unique opportunity to be part of a pioneering effort in the industry.
What are the most popular categories at Ripple?
Infographic showing various Data Scientist job openings at Ripple in the United States as of May 2026, with employment types broken down into 99% Full Time, and 1% Part Time. Highlights an 98% Physical, and 2% Remote job distribution.
Senior Staff Engineer - Data Engineering & Architecture

Senior Staff Engineer - Data Engineering & Architecture

Ripple

San Francisco, CA • On-site

$124.90K - $169.70K/yr

Other

Posted 11 days ago


Job description

At Ripple, we're building a world where value moves like information does today. It's big and bold, and we're already doing it. Through our crypto solutions for financial institutions, businesses, governments, and developers, we improve the global financial system. We craft greater economic fairness and opportunity for more people in more places globally. We perform the best work of our careers and develop our skills with colleagues who have our backs.

If you're ready to see your impact and embrace meaningful new challenges, join us, and build real world value.

The Work:

We're seeking a Senior Staff Engineer to act as the technical architect and strategic leader for Ripple's data platform. This role is one of the highest-impact individual contributor positions on the Data team. You will build a coherent, unified view of Ripple's data across all business units. You will establish the definitive source of truth for company metrics and lead our "AI for Data" transformation. You will collaborate across data teams and work directly with senior leadership in Data, Product, Engineering, and Go-to-Market to develop how Ripple understands itself through data. You won't just build systems; you will define the technical vision that connects them. This sits at the intersection of architecture, integration, governance, and applied AI.

What you'll do:

  • Build "One View" of Ripple by serving as the main architect for a Unified Data Fabric. Construct a well-rounded view of Ripple's data platforms, pipelines, and tools. Craft the systems and interoperability standards that bring data together across all business units for leadership, product, and GTM teams.
  • Compose and build the Ripple Metrics Store, a definitive, governed layer that serves as the single source of truth for business metrics across all business units.
  • Define metrics governance, ownership models, and validation frameworks. Build and maintain the Metrics Glossary in partnership with the Data Science team, and collaborators crafting a shared language for how Ripple measures its business.
  • Define and lead the AI roadmap for the Data organization setting the vision for how AI transforms the way Ripple builds, governs, and consumes data.
  • Enable conversational analytics and intelligence by architecting AI Agents that allow non-technical collaborators to self-serve insights directly from the data platform.
  • Automate data engineering workflows by encouraging use of AI-powered coding assistants for SQL/ETL generation. Define the standards, validation frameworks, and guidelines for AI-generated code across the team.
  • Lead cross-team build reviews, setting the technical bar for data architecture decisions across the organization.
  • Mentor and up-level engineers across data teams, raising the standard for system building, code quality, and engineering rigor.
  • Act as the main technical consultant to senior leadership across Data, Product, and Engineering regarding long-term technical bets, platform investments, and build-vs-buy decisions.

What you'll bring:

  • 14+ years of experience in data engineering, data architecture, or related software engineering fields, with a significant portion spent in senior technical leadership or architect roles.
  • Having a track record of bringing together data ecosystems from multiple business units, you have built the connections that convert isolated data platforms into a consistent entity.
  • Deep expertise in modern data architecture, data mesh, lakehouse patterns, with hands-on experience in technologies like Spark, Kafka, Airflow, dBT, Databricks, and cloud-native data services (AWS, GCP, or Azure).
  • Experienced in metrics governance and semantic layers, you have established or planned metrics stores, business glossaries, or comparable systems that provide an organization's source of truth.
  • Demonstrated experience with AI/ML in data engineering contexts, whether building feature stores, deploying AI-powered data quality monitoring, integrating LLMs into developer workflows, or crafting agentic systems for data operations.
  • Architectural vision paired with execution ability, you can whiteboard a multi-year platform strategy and also get into the code to prototype critical path components.
  • With outstanding communication and influence skills, you comfortably present to VP/C-level audiences. You translate technical trade-offs into business language and build consensus among teams with competing priorities.
  • A collaborative, ego-free approach to leadership, you lead through influence and technical credibility, not through title.
  • Experience with blockchain data, on-chain analytics, or large-scale financial/payments data is a plus.
  • Direct involvement with LLM-enabled developer tools and agents, AI code synthesis, RAG-style data catalogs, natural language querying interfaces, or agent orchestration solutions is beneficial.
  • BS or equivalent experience in Computer Science or similar.