Data Marketing Scientist in London

Teya was founded on a simple belief: local businesses deserve better.They are the cafés, restaurants, salons, shops and entrepreneurs that bring character to our high streets, create jobs and keep communities moving. Yet for too long, financial services has made life harder for them - with clunky tools, poor support and complexity that gets in the way of running a business.We’re building a financial platform for local businesses across Europe - one built around simple tools, thoughtful design and real human support. Our Members rely on us to help them run their business with confidence, and that responsibility shapes the way we work.We care about quality. And we believe great performance and genuine hospitality should go hand in hand.If you want to build meaningful products, solve real problems and make a genuine difference for local businesses, we’d love to hear from youData ScientistYour MissionWe're looking for a Data Scientist to join the Credit & Analytics team at Teya and help build the models that underpin our lending products. You will support the development of the credit risk and pricing models that help us make better decisions for the small businesses we serve across multiple geographies.You will work across the model lifecycle, from data exploration and feature development through to deployment and performance monitoring. Working alongside senior data scientists, you will learn how to develop models in a statistically sound way and see first-hand how they shape credit decisions, pricing and portfolio performance.You will collaborate with Credit Strategy, Analytics, Product, Engineering and Data Engineering, building your understanding of how data science creates real business value. This is an excellent opportunity for someone at the start of their data science career, with a strong quantitative background and a genuine interest in credit and lending, who wants to learn fast in a high-growth fintech.Build and test components of credit risk and pricing models, under guidance from senior team members.Data exploration: Explore internal and external data sources, clean and prepare real-world datasets, and engineer features that could improve our models.Analysis: Carry out analysis of merchant behaviour, repayment performance and product performance to answer well-defined business questions.Model monitoring: Help track model performance, flag changes or drift, and support investigations into what is driving them.Model deployment: Support Data Engineering with deploying models onto the ML Platform and integrating them into the credit decisioning system.Documentation: Document your work clearly so models and analysis are easy to understand, review and build on.Collaboration: Work closely with Product, Engineering, Data Engineering and Credit Strategy, and actively seek feedback from senior data scientists to develop your technical and commercial skills.Requirements0-2 years' experience in data science, quantitative analysis or a related field.Degree in a quantitative field such as Mathematics, Statistics, Physics, Engineering, Computer Science or a related discipline.Solid grounding in statistics and an understanding of core machine learning techniques.Working knowledge of Python and SQL, with experience handling and analysing real-world datasets.Exposure to credit risk modelling or financial services is a plus.The PerksWe trust you, so we offer flexible working hours, as long as it suits both you and your team.Physical and mental health support through our partnership with WellHub, giving free access to over 1,500 gyms in the UK, 1-1 therapy, meditation sessions, and digital fitness and nutrition apps.Our company offers extended and improved maternity and paternity leave choices, giving employees more flexibility and support.Cycle-to-Work Scheme.Health and Life Insurance.Pension Scheme.25 days of Annual Leave (plus Bank Holidays).Office snacks every day.Friendly, comfortable, and informal office environment in Central London.We are committed to creating an inclusive environment where everyone regardless of race, ethnicity, gender identity or expression, sexual orientation, age, disability, religion, or background can thrive and do their best work.