Data Scientist - Fraud Risk at Rain · Web3Vacancy
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Data Scientist - Fraud Risk

Remote
$190,000–$230,000
◆ 9/10 aianalyticscryptofintech
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About this role

Join Rain as a Data Scientist focusing on Fraud Risk, where you'll design real-time monitoring systems and implement predictive ML models to enhance fraud detection and customer experience in a fast-growing fintech environment.

Description

Rain 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. We 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. The 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.

Responsibilities

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.
Requirements

Qualifications

  • 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.).
  • 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.
Conditions & Benefits

What We Offer

  • Salary: $190K – $230K.
  • Offers Equity.
  • Offers Bonus.
Job Details
Salary$190,000–$230,000
LocationRemote
AI Score★★★★★★★★★☆ 9/10
PostedAug 28, 2026 (10d ago)
Tags & Skills
aianalyticscryptofintechpaymentsData Scientistremote
Tech Stack
SQLPythonFraud RiskEngineeringAnalyticsData ScienceCompliance analyticsIdentity riskAMLReal-time monitoringDashboardsFraud detectionRisk controlsML ModelsAI-assisted data analysisA/B testsPayments fraud
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