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 a Data Scientist to join our ambitious team, focused on discovering, designing, and experimenting with new estimators, models and features to boost payment performance across our portfolio of merchants. You will work closely with Data Scientists, Product and Engineering to enhance our core offering, protect customer lifetime value through network intelligence, and ensure safe model launches through robust observability. Key Responsibilities Contribute to the research and development of new ML models and estimators to boost core Acceptance Rate performance. Design and implement experiments to produce actionable insights, focusing on managing time-based data leakage and ensuring robust model evaluation. Collaborate with other Data Scientists and engineers to productionise ML features, models and evolve our evaluation and monitoring frameworks. Write high-quality, interpretable Python code for feature engineering and model training, contributing directly to our core products. Communicate hypotheses, evaluation results, and monitoring dashboards clearly to both technical and non-technical audiences. About You 3+ years of experience developing machine learning models to solve business problems. Strong understanding of supervised ML algorithms, tuning, and performance evaluation. Experience with a range of feature engineering techniques (e.g. target encoding). Solid grasp of frequentist and Bayes…