About the job
About Rain
Rain is pioneering the future of payments globally. Our dedicated and innovative team is committed to transforming payment systems by making stablecoins practical and accessible. We facilitate card transactions, cross-border payments, B2B transactions, remittances, and much more. By collaborating with fintech companies, neobanks, and institutions, we empower them to create global, inclusive, and efficient solutions. Join us at this high-growth company backed by leading investors across fintech, crypto, and SaaS sectors, including Sapphire Ventures, Norwest, Galaxy Ventures, Lightspeed, Khosla, and others. If you are enthusiastic, bold, and eager to contribute to a borderless financial future, we want to hear from you.
Our Philosophy
At Rain, we advocate for an open and flat organizational structure. Each team member is encouraged to pursue their aspirations and take ownership of their career advancement. We value creativity and initiative at all levels, allowing you to influence our company's roadmap and vision.
About the Fraud Risk Management Team
Our Fraud Risk Management team specializes in crafting advanced, scalable solutions to mitigate risks and ensure a seamless customer experience. Through comprehensive monitoring of transactions and lifecycle events, we enhance fraud detection and response efficiency. Our strategies are driven by machine learning models that support new products and guarantee their success. Rain's innovative payment technology presents new challenges that demand a holistic approach, strong data analytics, and adept fraud management skills.
Your Responsibilities
- Design and develop scalable machine learning systems focused on fraud detection, anomaly detection, and behavioral analysis.
- Establish and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, deployment, and ongoing monitoring.
- Create low-latency, real-time decision systems in collaboration with data scientists, integrating with transaction and behavioral data streams.
- Manage ML infrastructure encompassing model versioning, automated retraining, and secure deployment strategies.
- Develop robust performance monitoring and alert systems for model performance, latency, data integrity, and drift.
- Lead experiments focused on model explainability, drift detection, and adversarial robustness in fraud prevention initiatives.

