Data Scientist

Company Description We’re http://checkout.comCheckout.com http://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.

Checkout.com http://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.

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.

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 Bayesian statistics for parameter estimation and experimentation.
• Experience in writing clean, production-grade Python code for both model training and inference.
• Excited to leverage LLMs for coding support and process optimisation to maximise personal and team productivity.

Experience with advanced data transformation techniques (e.g., lambda functions).

• Familiarity with, or hands-on experience in, recommender systems, contextual bandits, or network intelligence applications.
• Experience in fintech, payments, or building cross-disciplinary relationships for advice and guidance.
• Familiarity with the unix shell, Databricks, Docker, and common cloud platforms (GCP/AWS).

Bring all of you to work We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one. Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands. We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.

It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.

Checkout.com http://Checkout.com We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection. Curious about what it’s like to be part of our team? Visit our Careers Page https://www.checkout.com/careers to learn more about our culture, open roles, and what drives us. For a closer look at daily life at Checkout.com http://Checkout.com, follow us on LinkedIn https://www.linkedin.com/company/checkout/life/ and Instagram https://www.instagram.com/checkout_com/

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