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A leading foreign exchange specialist in the UK is hiring a Data Scientist to leverage data insights and drive innovation. In this hybrid role, you will collaborate with cross-functional teams, develop predictive models, and contribute to strategic decisions. Ideal candidates will have 3+ years of experience in data science, expertise in Python and SQL, and excellent communication skills. This position encourages a diverse skill set and a creative approach to problem-solving.
Job Title: Data Scientist
Job Type: Full Time, Permanent
Location: London, Hybrid (3 office days a week)
At Travelex, data is at the heart of every decision we make. We rely on advanced analytics and deep insights to power our products, optimise our processes, and uncover new opportunities. We are seeking an experienced Data Scientist with strong statistical expertise, analytical curiosity, and creative problem-solving skills to join our growing team.
In this role, you will work closely with cross-functional teams to extract meaning from complex data, design predictive models, and translate insights into measurable business value. You’ll be instrumental in shaping how data informs strategy and drives innovation across the organisation.
To remain the world’s leading foreign exchange specialist, we are focused on making our customers’ lives simpler, more engaging and hassle free while they travel or move money abroad. We promise to give them the freedom and peace of mind to explore the world, their way – enabling them to travel confidently because they know they have us to lean on.
Customer centricity and digital are at the heart of our business strategy. Our commitment to innovation has never been greater, with the development of a number of digital-first, greenfield products and services. And with the Travelex's resources, deep industry experience and leading brand we are inventing the future of FX, cross-border e-commerce and international payments.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.