Data Science Lead – DSL1

Wise · London, England ·

Last confirmed by our partners on 24 Oct 2026.

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Overview

As Data Science Lead for the Verification Data Science team in KYC Global and Onboarding, you drive the data strategy to strengthen customer onboarding while enabling a seamless experience. You will mentor scientists, steer the ML lifecycle from training to production, and shape models used for document verification.

Your work directly supports Wise’s mission to simplify money movement for millions of customers. You’ll collaborate across cross-functional teams to align data initiatives with business goals and risk mitigation. This role offers impact at scale, shaping how our KYC systems prevent fraud and support growth.

Pay / Benefits

  • Stock options
  • Hybrid working model
  • Annual personal development budget
  • Career growth / coaching
  • Diversity and inclusion

Responsibilities

  • Own and prioritise the data science roadmap in collaboration with cross-functional teams
  • Lead planning and ideation of data science projects with integrated teams
  • Identify opportunities to apply machine learning and data science techniques
  • Communicate value, limitations, and potential impact of data science initiatives to stakeholders
  • Manage and create large image and tabular datasets for KYC processes
  • Iterate on deep learning image models and optimize ML model performance
  • Maintain a large Python codebase following industry practices
  • Stay current with ML research and integrate relevant ideas into production systems
  • Research Document verification to prevent criminal activity
  • Coach, develop, and support hiring and career growth of data scientists

Key requirements

  • cross-functional collaboration
  • communication of complex concepts to diverse stakeholders
  • leadership and coaching
  • machine learning / deep learning
  • production ML systems
  • data engineering for large datasets
Apply on partner site Listing supplied by one of our partner job boards. Last confirmed: 4 days ago.

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