Director, Data Science - Payment Performance

Job description

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. THE OPPORTUNITY Checkout.com http://Checkout.com helps businesses thrive in the digital economy by making payments work better. Within Payment Performance, Data Science is central to how we improve acceptance, reduce payment costs and protect merchants and Checkout from fraud. We are looking for a Data Science leader to bring our Fraud and Acceptance Data Science teams together behind one strategy and way of working. The teams already exist and are building important products. Your job is to help them work as one organization, raise the bar for how models are developed and operated, and turn more of their work into measurable production impact. You will lead a distributed group of Data Scientists working on real-time decisioning at global payments scale. The problems range from fraud detection and adaptive risk strategies to authorization optimization, routing and recovery. The common thread is using machine learning to make better decisions on every payment. This is a player-coach role. You will set direction and develop the organization, but remain close enough to the work to challenge technical choices, lead design reviews and contribute directly when the problem calls for it. WHAT YOU’LL DO SET THE DATA SCIENCE STRATEGY - Create one Data Science strategy across fraud and payment optimization, connected to the wider Payment Performance vision. - Decide where we should invest across models, data, experimentation and shared capabilities to create the greatest business impact. - Build common technical standards and ways of working without forcing different problem domains into the same approach. - Identify where new scientific methods or data assets could create a lasting competitive advantage. TURN MODELS INTO PRODUCTION IMPACT - Own the end-to-end model lifecycle, from problem framing and experimentation through evaluation, launch and production performance. - Improve measurable outcomes across fraud, acceptance and payment cost, with models that operate safely and reliably at scale. - Ensure teams optimize for business and merchant outcomes rather than research output or offline model metrics alone. - Raise the standard for experimentation, explainability, monitoring, drift detection and model iteration. - Work with Engineering to make productionization a shared responsibility: Data Science owns model performance, while Engineering owns platform reliability and deployment infrastructure. PARTNER WITH PRODUCT AND ENGINEERING - Jointly prioritize the roadmap with the Fraud and Intelligent Acceptance Group Product Managers. - Ensure Product owns merchant problems, desired outcomes and commercial trade-offs, while Data Science owns scientific direction, model quality and technical standards. - Translate complex scientific choices into clear product, customer and investment decisions. - Partner with Engineering leaders on the architecture and capabilities needed for low-latency, resilient ML systems. - Spend time with merchants when direct technical engagement can uncover a better problem, build confidence or demonstrate value. LEAD AND GROW THE ORGANIZATION - Lead, coach and develop Data Scientists across Fraud and Acceptance, initially managing much of the team directly. - Create clear ownership, goals and development paths across a distributed organization. - Develop future domain leads who can take on more day-to-day technical and people leadership as the teams grow. - Hire exceptional scientists and build a culture that combines scientific rigor, pace, curiosity and accountability. - Create an environment where senior scientists can challenge assumptions, do their best work and connect it to outcomes that matter. STAY CLOSE TO THE SCIENCE - Act as a technical sounding board for senior scientists across modelling, experimentation and ML-system design. - Lead or contribute to design reviews, prototypes and selected analyses where your involvement can materially