Data Scientist Marketing
Description
At bunq, we’re on a mission to Make Life Easy for our users by creating the most user-centric banking experience out there. Our paid marketing campaigns run on predictions about which new users will turn out to be valuable. This role makes sure those predictions are right.
That’s where you come in. As our Data Scientist, Marketing, you’ll own the machine learning models that predict the value of every newly verified user and feed that value into Google Ads, so our campaigns find more users like them. If the models are off, the spend is off. AI can help you write and debug code, but it can’t build the model or judge whether the output can be trusted. That judgment is yours.
Take Ownership 🚀
You’ll own the predictive models that steer our marketing spend. Here’s how you’ll do it:
Build, maintain and expand the predictive models for marketing, from first idea to production, starting with the models that predict each new user’s value in their first 90 days.
Hit the model quality targets within 9 months: a Spearman rank correlation of 0.50 or higher and a binary model AUC of 0.75 or higher.
Keep the models running in production and monitor data and model health to find where to intervene, using Claude Code to move faster and validating everything it produces.
You’ve built predictive models with traditional machine learning, such as XGBoost.
You’ve written Python and built the data pipelines a model needs, including when none existed.
You’ve shipped ML models to production and kept them running.
You’ve done deep dives into data and model health to find what needs intervention.
You use AI tools like Claude Code in your daily work and know where they stop.
You are fluent in English.
Curious to see how we make life easy? - try the bunq app, it only takes 5 minutes to sign up.
Your space to perform
We give you the space and the tools you need to succeed 💪
🤟 Great, international colleagues who share your mindset
👩💻 Hybrid setup: after 3 months in-office, work 2 days remote, 3 days in-office weekly.
🧳 Digital Nomad Program: After your first year, enjoy up to 20 days per year to work while traveling, combining flexibility with strong team collaboration
🚀 We reward tenure with a dedicated travel budget: €1.5k after 2 years and €3k after 4 years to visit another core office.
📚 We support growth with bunq Academy and €1500 annual learning budget
🧘 €60 monthly allowance via Alleo by Epassi, rolling over monthly and resetting annually
🚌 Travel expenses are covered whether you come walking or by bike, bus or car (though we prefer green choices 🌳)
💻 A MacBook so you can Get Shit Done with us
🥦 Delicious lunches from our fabulous in-house chefs with vegan and vegetarian options
💰 An optional pension plan with monthly contribution from bunq
💸 Monthly contribution to your phone and internet bills
🍻 Friday drinks and other celebrations - bunq style
All new hires are subject to Pre-employment Screening (PES), which includes checks conducted by our third-party partner, Zinc. This is part of our commitment to a secure and trustworthy workplace
Skills
As published by recruitee · 13 questions
Basics
Full name, Email, Phone
Short answers (2)
- What country do you currently live in?
- What are your salary expectations? (gross, annual, in EUR)
Pick from a list (11)
- Which visa situation fits you best?
- How do you assess your writing and speaking English skills?
- Do you agree and confirm with the above mentioned terms (contract duration, hybrid way of working, and standard working hours)?
- Have you previously worked at bunq?
- How do you usually approach your work, its impact, and solving challenges?
- Which of the following statements best describes your approach to using AI in a professional context?
- How familiar and practised are you with building predictive models using traditional machine learning (e.g. XGBoost, random forests, regression)?
- How familiar and practised are you with deploying ML models to production and keeping them running?
- How would you rate your practical proficiency with Python for data science, including building data pipelines?
- Please indicate your proficiency in English (speaking, reading, writing):
- How did you first hear about this role?