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Shift

AI-driven SaaS for fraud detection

Data Scientist

Applied Machine LearningUpdated on Nov 12
Full-timeParis, FranceNo salary listed

About the Job

Requirements

  • Proficiency in writing maintainable, production-quality code; a computer science degree and/or experience developing applications with actual users may demonstrate this capability.
  • Understanding of statistics and machine-learning problems, tasks, and common solutions; a degree in machine learning and/or statistics may demonstrate this capability.
  • Comfort communicating with clients regularly.
  • Interest in learning and mastering the business aspects of the products.
  • Fluency in English.
  • Advanced written and spoken French at a level equivalent to a native speaker.

Responsibilities

  • Contribute to the insurance roadmap and work with clients using structured data, free text, documents, and images.
  • Implement data engineering processes, typically from client data extracts through insertion into data stores such as SQL and Elasticsearch.
  • Develop, test, and tune models, and put them into production for tasks such as fraud detection and automation detection in complex environments.
  • Automate key business tasks by implementing them in the production process framework in C#.
  • Meet with clients and interact with external stakeholders to gather direct user feedback, present business cases, and define the roadmap for product evolutions.
  • Contribute to improving and rolling out the Fraud Detection solution.

About the Company

Shift

AI-driven SaaS for fraud detection

Shift provides AI-driven SaaS solutions for the insurance industry to detect fraud and automate claims processing. Its software analyzes large amounts of data with machine learning models to spot fraudulent activity and to route and accelerate claims through automated workflows. Subscriptions give insurers access to the platform, with additional consulting and support services available. Compared with others in the space, Shift targets a broad range of insurers—from large multinationals to regional providers—focusing on scalable, data-driven automation rather than single-point tools. The company’s goal is to help insurers reduce costs, improve the accuracy of decisions, speed up claims handling, and boost customer satisfaction.

Company size
501-1,000
Company stage
Series D
Total funding
$318M
Headquarters
Paris, France
Founded
2013

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