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Vacantes / Ripple

Staff Data Scientist

Ripple · Londonsenior

En corto

  • ▸Científico de datos técnico principal que define marcos analíticos escalables en Ripple.
  • ▸Día a día: define estrategias de medición, construye frameworks reutilizables y acelera análisis con IA.
  • ▸Destacado: liderazgo técnico en productos de cripto, finanzas y redes on-chain, con foco en causalidad y crecimiento.

Fluency in English required

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¿Qué piden?

  • ✓8+ años en ciencia de datos o análisis cuantitativo
  • ✓liderazgo técnico en equipos multifuncionales
  • ✓experiencia comprobada en frameworks analíticos escalables
  • ✓uso práctico de IA para acelerar análisis (LLMs, flujos autónomos)
  • ✓expertise en experimentos, inferencia causal y modelado estadístico
  • ✓dominio de Python/R, SQL y big data (Databricks, Airflow, dBT)

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonRSQLDatabricksAirflowdBTLLMsagentic workflowsnatural-language data interfaccausal inference

¿A quién escribirle en Ripple?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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 levelling 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 Treasury, Markets, Custody, and XRPL. • 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. • Drive evidence-based evaluation of growth across customers, corridors, and on-chain activity, surfacing the causal drivers behind adoption and volume. • Define and communicate the metrics leadership runs on : translating complex results into clear narratives for Ripple executives and external stakeholders. • Raise the bar for the DS function through thought leadership and mentorship across embedded teams. WHAT YOU'LL BRING: • 8+ years in data science or quantitative analysis, with a track record of senior level impact across multiple teams. • Demonstrated technical leadership across cross-functional teams , influencing roadmaps and strategy at both 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, fluency in SQL, and experience with large-scale data tech (Databricks, Airflow, dBT a plus). • Experience with FinTech, payments, crypto, or blockchain data is a strong plus. • Advanced degree (MS, PhD) in a quantitative field preferred. • E

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