Senior Data Scientist

Job description

Company Description We’re Checkout.com . You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day. We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers. Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale. If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact. With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started. About the Role Checkout.com is looking for an experienced Senior Data Scientist to lead advanced optimisation projects and champion the development of robust, sustainable ML architectures. You will be responsible for the enhancing payment flow through data and ML services, advancing the complex problem of multi-objective optimisation. You will work closely with other Senior and Staff Data Scientists to shape the architecture of our decisioning systems. Key Responsibilities Lead projects and architecture design for multi-objective optimisation and scalable models. Create custom loss functions, evaluation, and tuning frameworks that reflect complex business problems. Collaborate with product stakeholders to align technical strategy with business goals and resolve blockers across the team. Apply efficient data transformations using distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale business problems. Deep understanding of frequentist and Bayesian statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects). Expertise in model explainability to tune feature engineering…