• Translate business priorities into scalable data capabilities • Establish enterprise data standards • Lead cross-functional delivery across business and technology teams • Serve as the data engagement lead for an assigned business function • Co-develop and execute the enterprise MDM and Reference Data roadmaps • Lead MDM and Reference Data initiatives across critical domains • Establish governance processes, data standards, stewardship responsibilities, and quality controls • Lead the implementation and enhancement of Reltio MDM capabilities • Lead the implementation and enhancement of enterprise Reference Data capabilities • Advance semantic and terminology management capabilities • Partner with business teams, Data Governance, Architecture, Data Engineering, and other technology groups • Lead internal delivery teams and external partners

About the CompanyRain makes the next generation of payments possible across the globe. We’re a lean and mighty team of passionate builders and veteran founders. Our infrastructure makes stablecoins usable in the real-world by powering card transactions, cross-border payments, B2B purchases, remittances, and more. We partner with fintechs, neobanks, and institutions to help them launch solutions that are global, inclusive, and efficient. You will have the opportunity to deliver massive impact at a hypergrowth company that is funded by some of the top investors in fintech, crypto, and SaaS, including Sapphire Ventures, Norwest, Galaxy Ventures, Lightspeed, Khosla, and several more. If you’re curious, bold, and excited to help shape a borderless financial future, we’d love to talk. Our EthosWe believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company. About the TeamThe fraud risk management team at Rain creates sophisticated, scalable risk mitigation solutions to protect our customers and provide them a low-friction experience. We achieve this by maintaining transaction and lifecycle event monitoring, building alerts to speed up fraud detection and response, and creating risk rules and strategies empowered with ML models. We are a pillar of the business supporting new products and ensuring their success. Rain’s next generation payment technology brings unique and new fraud vectors requiring holistic, end-to-end thinking, strong data fundamentals and fraud management savvy to combat. What you’ll do - Own the metrics, dashboards, and reports that inform business strategy and decision-making - Design and implement real-time monitoring systems for fraud signals and alerting thresholds - Design, simulate, and implement real-time risk controls using real-time and offline predictive ML models - Balance risk controls with customer experience by focusing on loss savings, reduced user friction, and margin improvement - Partner with go-to-market teams to demonstrate the technical and business value of risk controls to customers - Effectively summarize complex analytical results to leadership and non-technical stakeholders - Assist partners when they have fraud concerns with their traffic What we're looking for - Advanced proficiency in SQL and Python - 3+ years hands-on experience working on automated fraud risk strategies and experimentation (A/B tests), including implementation of real-time and offline predictive ML models - Skilled in designing, building, and maintaining dashboards and fraud risk metrics - The ability to manage ambiguous problems and find solutions - High autonomy, including the ability to prioritize your workload effectively - Relevant quantitative bachelor’s (or higher) degree (Statistics, Mathematics, Engineering, Computer Science, etc.) Nice to have, but not mandatory - Experience at a high-growth fintech company or financial services company - Exposure to customer-facing or GTM-facing analytics or fraud issue triaging - Background in identity risk, AML, payments fraud, or compliance analytics - Fluency in AI-assisted data analysis / data science tooling Things that enable a fulfilling, healthy, and happy experience at Rain: - Unlimited time off 🌴 Unlimited vacation can be daunting, so we require Rainmakers to take at least 10 days off. - Flexible working ☕ We support a flexible workplace. If you feel comfortable at home, please work from home. If you’d like to work with others in an office, feel free to come in. We want everyone to be able to work in the environment in which they are their most confident and productive selves. New Rainmakers will receive a stipend to create a comfortable home environment. - Easy to access benefits 🧠For US Rainmakers, we offer comprehensive health, dental and vision plans for you and your dependents, as well as a 100% company subsidized life insurance plan. - Retirement goals💡Plan for the future with confidence. We offer a 401(k) with a 4% company match. - Equity plan 📦 We offer every Rainmaker an equity option plan so we can all benefit from our success. - Rain Cards 🌧️ We want Rainmakers to be knowledgeable about our core products and services. To support this mission, we issue a card for our team to use for testing. - Health and Wellness 📚 High performance begins from within. Rainmakers are welcome to use their card for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you! - Team summits ✨ Summits play an important role at Rain! Time spent together helps us get to know each other, strengthen our relationships, and build a common destiny. Expect team and company off-sites both domestically and internationally.

• Design, implement, and lead the rollout of a pragmatic, scalable, and adaptable enterprise Data Governance Framework. • Develop supporting policy and standards architecture (e.g., data classification, metadata management, data access and usage policies). • Embed governance by design into data pipelines, platforms, and data product development. • Lead the creation, approval, and adoption of data policies in close collaboration with Legal, Privacy, Security, GRC, and the Data Team. • Ensure alignment of governance policies with regulatory and contractual requirements (e.g., GDPR, CCPA, internal risk policies). • Develop control frameworks to assess compliance and maturity across data domains. • Establish enterprise-wide data quality standards, metrics, and processes for issue detection and remediation. • Empower domain-aligned data stewards and owners by providing guidance, training, and governance tools. • Champion accountability and ownership of key data elements across the organization. • Collaborate with business unit leaders, product managers, and engineers to operationalize governance policies across systems and workflows. • Drive adoption of governance principles across federated data teams through forums, working groups, and training. • Clearly articulate the business value and regulatory importance of data governance to technical and non-technical stakeholders. • Support change management initiatives to increase understanding, adoption, and integration of governance practices into daily operations. • Lead the Data Governance Council and Data Steward Forums to sustain engagement and drive accountability. • Recommend and support the implementation of data governance tooling (e.g., catalog, lineage, glossary, policy manager) in partnership with the Data Team. • Define and track KPIs to assess framework rollout progress, data quality trends, and policy adoption.

• Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure • Translate complex fraud patterns into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches • Design and maintain feature engineering pipelines for model development • Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain • Partner closely with leadership, go-to-market, fraud operations, product, and engineering teams to define and execute effective fraud strategies • Champion a culture of continuous learning, experimentation, and collaboration across the fraud and broader data science teams