Data Scientist - Customer Understanding

Wise; London; On-site; GBP 50,000 - 80,000; Full time

Job summary

A leading global technology company is seeking a Data Scientist for their Marketing team in London. This role involves identifying growth opportunities and developing models to enhance customer understanding and optimize marketing efforts. The ideal candidate should have strong skills in LTV modeling, Bayesian machine learning, and proficiency in Python. This position offers significant autonomy and the chance to make a direct impact in a diverse and inclusive environment.

Qualifications

• Experience with LTV modeling and marketing mix modeling.
• Knowledge of Bayesian machine learning approaches and neural networks (PyTorch preferred).
• Proficiency in Python and experience with APIs, Kafka, Docker.

Responsibilities

• Help Marketing team identify growth opportunities.
• Develop predictive models for Customer Lifetime Value to prioritize marketing efforts.
• Model customer behavior and product usage to target audiences effectively.

Skills

LTV modeling

Marketing mix modeling

Bayesian machine learning

Statistics

Python

APIs

Kafka

Docker

Job description

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Data Scientist - Customer Understanding, London

Client: Wise

Location: London, United Kingdom

Job Category: Other

EU work permit required: Yes

Job Reference: 14c4526d3d5d

Job Views: 7

Posted: 19.08.2025

Expiry Date: 03.10.2025

Job Description:

Company Description

Wise is a global technology company, building the best way to move and manage the world’s money. Min fees. Max ease. Full speed.
Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their life easier and save them money.
As part of our team, you will be helping us create an entirely new network for the world's money. For everyone, everywhere.

Job Description

Data Scientist - Marketing

We’re looking for a Data Scientist to join our growing Marketing Team in London. This role offers a unique opportunity to impact our growth, develop as a Data Scientist, and help save people money.

Your mission:

Wise has pioneered new ways for people to transfer money across borders and currencies. Our customers can also manage their money with the world’s first platform to offer true financial freedom. Your mission is to help make people aware of Wise as a solution for cross-border money needs.

Here’s how you’ll contribute to Marketing:

• Help the Marketing team identify growth opportunities
• Develop predictive models for Customer Lifetime Value (LTV) to prioritize marketing efforts
• Model customer behavior and product usage to target audiences effectively
• Use causal models to measure the impact of CRM and Invite campaigns on business metrics
• Assist in understanding which growth activities to invest in (Marketing Mix Models)
• Collaborate with Data Analysts to interpret and utilize models

Your daily tasks will include building and maintaining models, evaluating new ideas, and communicating insights about marketing performance.

This role will give you the opportunity to:

• Make a direct impact by partnering with marketing teams to reach millions
• Work autonomously, creating your own vision within the team
• Be part of a diverse team of Data Scientists, Analysts, and Marketers
• Contribute to our mission of making money without borders the new normal

Qualifications:

• Experience with LTV modeling and marketing mix modeling
• Knowledge of Bayesian machine learning approaches and neural networks (PyTorch preferred)
• Strong understanding of statistics, Bayesian reasoning, and causal inference
• Proficiency in Python and experience with APIs, Kafka, Docker
• Ability to own projects end-to-end and communicate data insights effectively

Additional Information:

At Wise, we value diversity, equity, and inclusion. We celebrate our international team and strive to create an environment where everyone feels respected and empowered. To learn more about life at Wise, follow us on social media.