Mads Overgaard
Copenhagen · mads.overgaard@example.com
4 November 2026
Ms Ingrid Holm
Head of Data Science
Fjordline Media
Subject: Application for Data Scientist
Dear Ms Holm,
Measured against a holdout group, the retention offers my churn model targeted cut cancellations by 9%. I would like to do that kind of work, where a model is judged by the decision it changes, as a Data Scientist at Fjordline Media.
At Nordlys Analytics I built the model in Python with LightGBM on 1,200,000 subscriber records, then moved it from a notebook to a scheduled Airflow pipeline with monitoring, so drift alerts reach the team before a stale score reaches a campaign. With product teams I have designed and run six A/B tests and written up the decision that followed each one. Earlier, at Sund Retail Group, my demand forecast for 800 products reduced stock-outs by a quarter over six months.
Fjordline building its first data science team is what drew me, and subscriptions are the business I know best from the churn side. A team of five needs shared habits early, which is why the line on documenting features and reviewing teammates' code matters to me. I already review colleagues' code and keep features in a shared catalog so others reuse them instead of rebuilding them. Each month I also explain results to non-technical managers in a plain-language review, and an editorial audience would get the same treatment.
Which subscription decision would your first model support, and how will Fjordline judge whether it worked? Those two answers would shape my first month, and in an interview I could show how I set up the holdout group behind the 9% figure.
Kind regards,
Mads Overgaard