{"id":2624197,"url":"https://alion.io/job/schibsted-data-scientist-subscription-data-enablement","title":"Data Scientist – Subscription Data Enablement","company":{"id":44405,"name":"Schibsted","domain":"schibsted.com","url":"https://alion.io/company/schibsted","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":{"grade":"C","score":67,"open_postings":3,"ghost_share":0.333,"stale_share":0.333,"repost_share":0.667,"time_to_fill_p50_days":19,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bergen, Norway","Stockholm, Sweden","Oslo, Norway"],"countries":["NO","SE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false}],"status":"live","first_seen_at":"2026-10-15T07:57:42Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-16T09:29:25Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Our paywall is a model. Come and make it smarter.\nSchibsted | Oslo, Bergen or Stockholm (hybrid)\nWe built our own dynamic paywall and offer engine. Now we need to make it better, understand what works, and bring it to more brands.\nWe'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.\nWhy this work matters\nOur democracies depend on independent journalism, and that is our business. Independent journalism needs readers who choose to pay for it.\nOur 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.\nDirect relationships with readers and digital profitability are how Schibsted measures success. Your models sit right in the middle of both.\nWho we are\nWe are the data and analytics team for Schibsted's Subscription business in Norway and Sweden: analysts, data engineers, an ML engineer and data scientists.\nWe 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.\nIt 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.\nA 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.\nWe want you to feel the same way about us a year from now.\nWhat you will do\nMake 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.\n\nFind 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.\n\nBuild 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.\n\nMake the results understandable. Explain findings to commercial colleagues with clear analysis and dashboards where they help.\n\nImprove 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.\n\nAsk good questions. Including “does this actually make us money?” when someone needs to.\n\nWhat you bring\nPython or R and SQL, used to analyse data and build models.\n\nHands-on experience developing and evaluating machine learning models, and an understanding of what it takes to run them in production.\n\nA solid foundation in statistics and experimentation, including hypothesis testing and A/B tests.\n\nThe 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.\n\nHow we work\nWe 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.\nIn 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.\nWhat you can expect from us\nReal ML problems, with models that influence decisions across some of the Nordics’ best-known news brands.\n\nExperienced colleagues to learn from, and room to take ownership of your work.\n\nTime and space to learn, experiment and share ideas.\n\nThe practical bits\nLocation: Oslo, Bergen or Stockholm. Hybrid.\n\nEmployee terms and benefits follow Schibsted’s standards in your country, and we are happy to go through the details with you in the process.\n\nThe team is spread across Norway and Sweden, so some days are on video.\n\nWorking language: English\n\nWe welcome applications from people with different experiences and career paths, and we focus on the skills and potential you bring.\nApply\nSend 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?\nIt 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.\nNo long cover letter needed.\nWe'll keep you informed throughout the process and let you know the outcome of your application.\nDeadline: November 6th 2026\nQuestions? Contact Johanna Haglund Lindqvist, Head of Data Science & Data Engineering.\nAbout Schibsted\nSchibsted 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. 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