Data Scientist – Subscription Data Enablement

Our paywall is a model. Come and make it smarter.

Schibsted | Oslo, Bergen or Stockholm (hybrid)

We built our own dynamic paywall and offer engine. Now we need to make it better, understand what works, and bring it to more brands.

We're looking for a Data Scientist who enjoys building models, testing ideas and finding out whether the results actually make a difference. You'll work alongside experienced data scientists, analysts and engineers, with opportunities to take ownership of your work and grow your skills along the way.

Why this work matters

Our democracies depend on independent journalism, and that is our business. Independent journalism needs readers who choose to pay for it.

Our job in Subscription Data Enablement is to understand those readers well enough to offer them the right thing at the right time, and to leave them alone when we shouldn't bother them.

Direct relationships with readers and digital profitability are how Schibsted measures success. Your models sit right in the middle of both.

We are the data and analytics team for Schibsted's Subscription business in Norway and Sweden: analysts, data engineers, an ML engineer and data scientists.

We work with brands such as VG, Aftenposten, Aftonbladet, Svenska Dagbladet, Bergens Tidende and E24. Our models already cover churn, renewal, segmentation and engagement. The dynamic paywall and offer engine is the newest addition to the family.

It is also a good test of how we work. The Dynamic Paywall project has many moving parts and competing priorities, and it needs people from data, product, tech, business and editorial teams in the same room.

A consultant who recently worked with us said that, despite technically being a consultant, they «never really felt like one». They described a team where everyone brings different expertise, ideas get challenged, debates are healthy, and we don't always agree, but there is «always mutual respect» and a shared goal of finding the best way forward.

We want you to feel the same way about us a year from now.

• Make our paywall smarter. Develop and improve the models behind our dynamic paywall and offers: who sees which offer, and when. Propensity, recommender and offer optimisation problems are all in scope.
• Find out what works. Design, run and analyse experiments with our brands, and report the results as they are, including what they don’t prove. Uplift modelling and causal inference are welcome tools here.
• Build models that get used. Take models from notebook to production with our data scientists and engineers: feature engineering, training, evaluation, testing, and monitoring once they’re live. We work with tools such as Snowflake, MLflow and Streamlit.
• Make the results understandable. Explain findings to commercial colleagues with clear analysis and dashboards where they help.
• Improve how we work. Bring ideas on modelling, tooling and MLOps as more brands come on board, including how AI can make our own work easier.
• Ask good questions. Including “does this actually make us money?” when someone needs to.

• Python or R and SQL, used to analyse data and build models.
• Hands-on experience developing and evaluating machine learning models, and an understanding of what it takes to run them in production.
• A solid foundation in statistics and experimentation, including hypothesis testing and A/B tests.
• The ability to explain your work to people who don’t speak ML, and the curiosity to learn from people who know the business better than you do.

We follow a short list of team principles, and we mean them: be curious and keep learning, deliver quality together, design collaboratively, help early and ask early, share generously, have fun and stay open.

In practice, that means Show n Tell and Friday data talks every other week, a monthly hack day, and an expectation that you ask for help before you’re stuck, not after. We also encourage sitting in on newsroom meetings. It’s hard to build a good model of what readers value without knowing what the newsrooms think is important.

• Real ML problems, with models that influence decisions across some of the Nordics’ best-known news brands.
• Experienced colleagues to learn from, and room to take ownership of your work.
• Time and space to learn, experiment and share ideas.

The practical bits

• Location: Oslo, Bergen or Stockholm. Hybrid.
• Employee terms and benefits follow Schibsted’s standards in your country, and we are happy to go through the details with you in the process.
• The team is spread across Norway and Sweden, so some days are on video.
• Working language: English

We welcome applications from people with different experiences and career paths, and we focus on the skills and potential you bring.

Apply

Send your CV and a few lines about a data science or machine learning project you've worked on. What problem were you trying to solve, what was your contribution, and what did you learn?

It doesn't have to be a model you took all the way to production. We're interested in how you think and what you've learned from doing the work.

No long cover letter needed.

We'll keep you informed throughout the process and let you know the outcome of your application.

Deadline: November 6th 2026

Questions? Contact Johanna Haglund Lindqvist, Head of Data Science & Data Engineering.

Schibsted is the largest news provider in the Nordics, with brands such as VG, Aftonbladet, TV4, MTV, Aftenposten, Svenska Dagbladet and Podme. Every day we inform, engage and entertain millions of people across Sweden, Norway and Finland. Since 1839, trust has been our foundation.