Data Scientist

Are you passionate about data analytics and building predictive models that solve real business challenges

Experians Nordic Analytics team is looking for a Data Scientist to help develop innovative credit risk solutions and analytical products across the Nordics. This is an exciting opportunity to work with statistical modelling machine learning and large-scale data while developing expertise in one of the most impactful areas within financial analytics.

Whether youre building on a strong academic foundation or already have experience within credit risk analytics were looking for someone who is curious technically skilled and motivated to continue growing within data science and credit risk analytics.

What Youll Do

As part of our Nordic Analytics team you will contribute to the development of data-driven solutions that help clients make smarter and more informed decisions.

Your key responsibilities will include:

• Delivering analytics projects with a primary focus on statistical and predictive modelling applying regression techniques machine learning and other analytical methods to solve business challenges.
• Developing testing and validating predictive models credit risk scorecards and other analytical solutions to support better decision-making.
• Developing new credit risk-related scores attributes and use cases for Experians analytics products and platforms.
• Exploring and analysing data to identify trends patterns risks and opportunities transforming complex data into actionable insights.
• Supporting client discussions to understand the clients problem identifying analytics needs and position scorecards statistical models and data analysis linked to business value.
• Communicating analytical findings and recommendations to both technical and non-technical stakeholders translating complex analyses into business value.
• Collaborating with colleagues across the Nordics on client projects innovation initiatives and the continuous development of Experians analytics capabilities.

Qualifications :

Who Were Looking For

Were looking for someone with strong analytical capabilities a solid technical foundation and a genuine interest in predictive modelling and data science.

We believe youll bring:

• A Bachelors or Masters degree in Statistics Mathematics Data Science Computer Science Economics Engineering or another quantitative field.
• Exposure to credit risk modelling scorecard development model validation or risk analytics.
• Hands-on experience in data analysis statistics predictive modelling or machine learning gained through professional employment.
   
• Strong programming skills in Python and SQL.
• A structured and analytical approach to problem-solving.
• Curiosity initiative and a desire to continuously learn and develop.
• Strong communication skills and the ability to explain complex analytical concepts clearly.
• Professional proficiency in English.

Nice to Have

• Experience within financial services banking fintech consulting or other data-driven industries.
• Experience with PySpark cloud-based analytics environments or large-scale data processing.
• Knowledge of credit risk management regulations.

Ytterligere informasjon :

Why Join Experian

At Experian youll join a collaborative and highly skilled analytics team that combines deep industry expertise with a passion for innovation.

Youll have the opportunity to:

• Work on impactful modelling and analytics projects across the Nordics.
• Learn from experienced analytics professionals and subject matter experts.
• Develop expertise in predictive modelling and credit risk analytics.
• Work with large and unique datasets within a global organisation.
• Grow your career in an international inclusive and supportive environment.
• 6 weeks Vacation

If youre motivated by solving complex problems through data and want to build innovative analytics solutions with real-world impact wed love to hear from you.

Experian Careers - Creating a better tomorrow together

Find out what its like to work for Experian by clicking here

Remote Work :

No

Employment Type :

Full-time