Ravelin

👥 101-250💰 $20,650,880 Series C over 4 years agoFraud DetectionTransaction ProcessingSaaSPaymentsCyber Security💼 Private Company
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Ravelin is a leading fraud detection and prevention platform protecting online businesses globally. We leverage advanced machine learning, network analysis, and data science to provide highly accurate fraud scores, enabling clients to streamline their fraud detection and prevention efforts. Our unique approach combines automated prevention with insightful explanations of fraud decisions, giving users not only protection but also valuable intelligence on evolving fraud tactics. We're a rapidly growing company with a strong engineering culture prioritizing collaboration, innovation, and work-life balance. Our tech stack includes Python, TensorFlow, Kubernetes, and Docker, and we adhere to best practices in ML engineering, making model deployment as efficient and reliable as code deployment. We're committed to building robust and adaptable models that consistently stay ahead of evolving fraud schemes. Ravelin values empathy, ambition, unity, and integrity, fostering a friendly and supportive remote-first environment. We offer competitive benefits including equity, flexible working hours, and a significant annual learning and wellness budget. We are backed by significant funding and consistently outperform industry norms in fraud detection. Join a team that's making a tangible impact on the global fight against online fraud and shaping the future of online security.

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📍 United Kingdom

🧭 Contract

🔍 Fraud detection

  • Experience building and deploying ML models using the Python data stack (numpy, pandas, sklearn).
  • Understand software engineering best practices (version control, unit tests, code reviews, CI/CD) and how they apply to machine learning engineering.
  • Strong analytical skills.
  • Being a strong collaborator with colleagues outside of your immediate team, for example with client support teams or engineering.
  • Being skilled at communicating complex technical ideas to a range of audiences.
  • The ability to prioritise and to manage your workload.
  • Being comfortable working with a hybrid team.

  • Build out our model evaluation and training infrastructure.
  • Develop and deploy new models to detect fraud whilst maintaining SLAs.
  • Write new features in our production infrastructure.
  • Research new techniques to disrupt fraudulent behaviour.
  • Investigate model performance issues using your experience of debugging models.

DockerPythonKubernetesMachine LearningNumpyPandasCI/CD

Posted about 24 hours ago
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