Data Scientist - GROUP BUSINESS ANALYTICS
As a market-leading credit management company, Intrum has wide-ranging expertise in debt collection services. We do business in 24 European countries and we have more than 8,000 experienced employees in our group. At Intrum we create value for our clients, our customers, our owners and for society at large. We are driven by our Purpose and our four values; Empathy, Ethics, Dedication, Solutions which define who we are and determine how we act. We make sure that companies get paid, help people get out of debt and enables for society to grow. If you want to join our team, apply today!
We are the Group Risk Team for the largest credit management company in Europe. We create & validate underwriting models, take active role in the investment process, develop solutions for business, train the analysts in the local subsidiaries and provide analytical support for the Group Management Team
In our team you will be responsible for applying the machine learning algorithms to support the investment decision. You will use your analytical skills to transform the data into business insights and present them to the top executives of the group as reports, visualizations, data science apps and solution stories. You will create the new methodologies for distressed assets valuation and will take the lead in implementing them as a business processes. You will have a key role in training the data scientists in the company structures across the Europe. You will work in the very dynamic investment industry environment and will be exposed to the international investment projects. You will learn a lot about the investment process for distressed assets and you will contribute to its success.
- R proficiency (data crunching / modelling / visualization / markdown / shiny)
- 2-3 years of practical experience in machine learning
- Expertise in applying the statistical concepts to the business situations
- Strong analytical skills
- Experience in creating and leading the technical trainings
- MSc in Statistics / Computer Science or related area