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Data Scientist – Customer Understanding 326 views

Data Scientist/Senior Data Scientist – Customer Understanding

They’re looking for Data Scientists to join their growing Marketing and Product Teams in London. This role is a unique opportunity to have an impact on Wise’s mission, grow as a Data Scientist (developing cutting-edge techniques) and help save customers money.

Your mission:

Wise has already pioneered new ways for people to transfer money across borders and currencies. Their customers can also manage their hard-earned money with the world’s first platform to offer true multi-currency banking. Your mission is to help make people aware of Wise as a solution for cross-border money needs, and help Wise target their offering to better serve existing customers.

Here’s how you’ll be contributing to Marketing and Product

  • You will help the Marketing and Product tribes find the biggest growth opportunities by measuring the incremental effect that marketing campaigns and product changes have on business metrics
  • You will use causal inference to decide which campaigns and features should be delivered to each user. You will help own and develop our groundbreaking open-source framework for automatic causal model selection (auto-causality)
  • You will model customer behaviour data and product usage so we understand which audiences to target and how (LTV/churn modelling, neural-lifetimes package)
  • You will understand the varying needs and responses of different groups of the customers (Customer Segmentation, in particular using state of the art causal inference models)
  • You will work closely with Data Analysts and you will help them understand and use models that you build (Causal Inference, LTV, and Customer Segmentation models)

A bit about you:

  • You have a good understanding of causal inference concepts and have some experience with machine learning models for causal inference.
  • You are familiar with lifetime value (LTV) modelling and customer segmentation
  • You have experience with Bayesian approaches to machine learning, as well as with using neural networks, ideally PyTorch
  • You have a good understanding of statistics, in particular Bayesian reasoning, and can estimate how accurate your results are, but also know when to stop analysing and deliver results
  • 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 solid knowledge of Python, and are able to make and justify design decisions in your Python code; you can throw together a REST service or a UI if need be. You’ve used external data pulled via APIs before

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