Data scientist for highly successful machine learning team


Visma is Scandinavia’s largest software company and one of the biggest SaaS vendors in Europe. The VML Machine Learning team delivers state of the art machine learning products across Visma’s fleet of business systems - processing millions of business documents and millions of transactions every month.

The data science team is responsible for evolving our machine learning models - from idea and testing to production quality Tensorflow models that our engineering team can deploy in production. Our data science work is done in python and based on Tensorflow today - but we’re always looking at the tool landscape for inspiration on where to go next. We’re looking forward to your input.

All of our machine learning models are in production and in use by thousands of companies - which also means that we have a growing treasure trove of data as the basis for our research. 


What we are looking for

We’re looking for a data scientist with a solid academic background and real production experience.

  • A suitable university background in

    • Machine learning

    • Neural networks/deep learning

  • Hands-on experience with delivering models in a production environment using Python

  • Experience with the Tensorflow stack is a plus

While we value practical experience shipping production models - we’re very concerned with maintaining good boundaries between our science team and our engineering team. Our data science team has no ops responsibility - your goal is to do outstanding data science. 


What you can expect working for Visma

  • The autonomy of a startup with the backing of Scandinavia’s biggest software company

  • Interesting problems to solve

  • A great appetite for state of the art data science

  • A small, experienced and cohesive team

  • Data at scale, serving a massive, and growing market

  • A truly great place to work - with time to experiment, have fun and win


The team is located in Copenhagen - but we’re open to remote work  - preferably in the nordics.




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