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Signifyd is seeking a Data Scientist II to join its Data Science team in Mexico City. The team is responsible for building the models that power Signifyd's fraud detection engine. The ideal candidate will be hands-on and involved in all stages of solution development, from brainstorming to deployment. The role involves a mix of research, experimentation, and collaboration.

Responsibilities include:

  • Building production machine learning models that identify fraud
  • Designing new algorithms to optimize the Signifyd Commerce Protection Platform
  • Writing production and offline analytical code in Python and Java
  • Researching real-time emerging fraud patterns with the Risk Analysis team
  • Working with distributed data pipelines
  • Communicating complex ideas effectively
  • Collaborating with engineering teams to strengthen the machine learning pipeline
  • Mentoring other team members

Requirements include:

  • Bachelor's degree in computer science, applied mathematics, economics, or an analytical field
  • At least 3+ years of experience
  • Hands-on statistical analysis with a solid fundamental understanding
  • Designing experiments and collecting data
  • Writing code and reviewing others’ in a shared codebase, preferably in Python and Java
  • Practical SQL knowledge
  • Familiarity with the Linux command line
  • Fluent in English

Signifyd offers:

  • The opportunity to work on cutting-edge machine learning models for fraud detection
  • A collaborative and supportive team environment
  • The chance to make a real impact on the Signifyd Commerce Protection Platform
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Signifyd

Signifyd is a company focused on providing solutions for businesses. The company emphasizes creating positive candidate and employee experiences. Signifyd values detail-oriented and organized individuals who can adapt and learn quickly. The company utilizes tools like Google Suite and Applicant Tracking Systems, highlighting its focus on efficiency and data management. Signifyd's commitment to inclusivity extends to providing reasonable accommodations during the hiring process.