Lead Data Scientist - Marketing in London

Wise is a global technology company, building the best way to move and manage the world’s money. Min fees. Max ease. Full speed.

Your mission is to help make people aware of Wise as a solution for cross‑border money needs.

Here’s how you’ll be contributing to Marketing:

• You will help the Marketing tribe find the biggest opportunities for growth.
• You will help us understand in what growth activity to invest (Marketing Mix Models) and how to reduce the uncertainty on marketing measurement.
• You will do this by developing predictive models to calculate Customer Lifetime Value (LTV), aiding in the prioritisation of marketing efforts and resource allocation.
• You will model customer behaviour data and product usage so we understand which audiences to target and how.
• You will work closely with Data Analysts and you will help them understand and use models that you build (LTV or MMM models).
• Your average day will include building new models, maintaining models used by everyone in the marketing tribe, evaluating new ideas and communicating what models can tell us about how and why we grow.

This role will give you the opportunity to:

• Have a direct impact – you will closely partner with every marketing team within both Organic and Paid Acquisition and help millions of people and businesses learn about how Wise can help them.
• Work autonomously – we believe people are most empowered when they can act autonomously. So rather than telling you what to do, you’ll work with your team to create a vision of your own.
• Be part of a diverse team – you will work in a team of Data scientists, Analysts and Marketeers.
• Be part of our mission to make money without borders the new normal.

About you:

• You have expert knowledge of Python, and are able to make and justify design decisions in your Python code.
• You have built Marketing Mix Modelling (MMM) models and you are able to translate the results into actionable strategies.
• You have experience in building lifetime value (LTV) models.
• You are familiar with a range of model types, and know when and why to use gradient boosting, neural networks, good old linear regression, or a blend of these.
• You have a proficient understanding of statistics, in particular Bayesian reasoning.
• You are able to take ownership of a project and see it through from end to end, with past experience in doing so.
• You are data‑driven with a structural approach. You need to be able to prioritise the value you can add, and manage your time effectively.
• You see a bigger picture of business processes and can cut through vagueness to define precisely where and how a model would fit into our stack and what value it would add.
• You are comfortable with visualising and communicating data to various audiences, you easily articulate and present your ideas.

Some extra skills that are great (but not essential):

• You have a good understanding of causal inference concepts and have some experience with machine learning models for causal inference.
• You have hands‑on experience evaluating marketing campaigns through incrementality testing and geo‑experiments.