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Machine Learning Engineer/Data Scientist

Location: Luleå

The problems

BehavioSec power real-time analyzing of behavioral data for Fraud prevention and authentication. We analyze data collected from the user and validates it against a stored profile for that user and calculates a risk of fraudulent usage for the session.


This is a technical role, You’ll be a part of our R&D team both as a creative and technical contributor and you will be analyzing data, build new Models based on the data, and make sure the Models scale with the business. We encourage a multidisciplinary approach and we want to you to take responsibility for your models from idea to production.

What you’ll do

  • Work with a team with different backgrounds to frame problems, both mathematically and business wise.
  • Perform exploratory data analysis to gain a deeper understanding of the problem.
  • Construct and fit statistical, machine learning, or optimization models.
  • Write production modeling code; collaborate with Software Engineers to implement algorithms in production.
  • Analyze and Visualize findings and data to further enhance the product.

Ideally you have

  • Ideally you have a B.S. and/or M.S. and/or Phd. in a relevant field or experience in a relevant field.
  • Passion for solving unstructured and non-standard mathematical problems.
  • End-to-end experience with data, including querying, aggregation, analysis, and visualization.
  • Proficiency with programming languages.
  • Willingness to collaborate and communicate with others to solve a problem.

What happens next

  • You apply and we will review your CV.
  • We will book a time when we will send you a home test and also a meeting, afterwards where we will review the home test as well as a more general interview.
  • If that goes well we will book for you to meet the team in a more casual setting like a lunch with the team.
  • If all feels right we will then proceed to negotiate the contract.

Being a part of the BehavioSec team

Have Impact: We take on meaningful and challenging projects that affect fraud prevention on a global scale.

Collaborate: We work internally with people from a variety of backgrounds and locations — such as product teams, engineering teams, sales teams so we transcends geography which also is what our product aims to help our users do. We also work externally with our customers, often on site, to understand and solve their problems.

Have Independence: We trust each other to effectively manage time and priorities—we don’t micromanage. We want to give people the space to think for themselves.

Grow: We push ourselves and our peers to improve themselves and the world around them.