We’re looking for an exceptional Data Scientist / Algorithms Engineer to join our office, and help deliver the next generation of financing solutions for small businesses and consumers. We are a group of fun, creative, and motivated people that like to tackle challenges head on. If you have a strong desire to be part of a small, agile team, in an exciting and growing space, you’ll find your next job at Enness.

The ideal candidate will be a data enthusiastic with proven track record of solving complex data related problems, using advanced statistical and machine learning tools, in a real-world product development setting. Candidates should demonstrate strong statistical, mathematical and technical capabilities.


  • Develop and implement new statistical/machine learning models to identify performance and risk drivers in the credit portfolio
  • Enhance data collection procedures to include information that is relevant for building analytical systems
  • Apply advanced statistical and predictive modelling techniques to build, maintain, and improve multiple real-time decision-making systems – from research to full production deployment
  • Work closely with our teams across all products, verticals and markets.


  • Master’s Degree in Computer Science, Statistics, Applied Math or related fields
  • 5+ years of experience in implementing statistical/machine learning algorithms and statistical programming tools (Python/R/Matlab/SQL/Metadata)
  • 5+ years of practical experience with data warehousing ETL, data processing, database programming and data analytics
  • Ability to understand various data structures and common methods in data transformation
  • Excellent pattern recognition and predictive modelling skills
  • Analytical ability to translate business requirements into a technical solution
  • Ability to handle several tasks simultaneously
  • Team player

Enness provides financing solutions to businesses and consumers that the banks cannot help across the globe.

Our team reflects our strategy: we move fast, prefer simplicity, and challenge boundaries as we work and learn together.