Machine Learning Scientist, Personalize Intelligence

This is Adyen Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition. For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. Personalize & Adyen Uplift Adyen is looking for a Machine Learning Scientist to join our team in Chicago focused on Checkout Personalization. Every checkout is a dynamic decision: which payment methods to display to a shopper, and in what order. While maximizing conversion is a primary objective, it is not the only one. Payment methods carry significantly different transaction costs and fraud exposure, creating a delicate multi-objective trade-off. Steering shoppers toward lower-cost methods risks losing the sale, while optimizing conversion in isolation surrenders margin. The core mission of this role is not picking a side, but mathematically modeling, quantifying, and optimizing this balance. Operating on hundreds of millions of payments annually gives us an exceptional volume of signal, rapid feedback loops, and a short path from algorithmic iteration to tangible merchant value. Because we only observe shopper behavior on the options displayed, counterfactual reasoning, learning from partial/logged feedback (contextual bandits), and robust offline-to-online evaluation are fundamental daily necessities, not afterthoughts. About the Role We are looking for an Applied Machine Learning Scientist / Data Scientist who thrives in deep exploratory research, hypothesis generation, and rigorous empirical analysis. Your primary focus will be on the scientific and algorithmic core: diagnosing current model bottlenecks, formulating novel modeling approaches, designing experiments, and interpreting complex offline and online results. While our team has dedicated engineering strength to support large-scale distributed pipelines and platform infrastructure, you should possess solid programming fundamentals (Python/SQL) and sound software habits, enabling you to independently write, implement, benchmark, and ship your model code. The annual base salary range for this role is $177,000 - $230,000 plus RSUs; to learn more about our compensation philosophy, please click here . This position is based out of the Chicago office. What you'll do • Identify Improvement Opportunities: Deeply analyze large-scale, complex payment and behavioral datasets to uncover patterns, understand failure modes, and identify untapped leverage points for model improvements. • Research & Model Architecture: Formulate, develop, and benchmark machine learning algorithms tailored to multi-objective ranking, contextual bandits, and decisioning under uncertainty. • Rigorous Experimentation & Causal Analysis: Design robust offline validation frameworks, counterfactual evaluation pipelines, and online A/B tests to separate true treatment effects from observational biases. • Analyze & Interpret Results: Deconstruct experiment outcomes beyond top-line metrics, providing clear quantitative explanations of why models behave the way they do and how trade-offs impact merchant margin and conversion. • Bring Models to Life: Write clean, modular, and maintainable Python code to implement your models, benchmark them against existing baselines, and collaborate with engineers to guide them through production integration. • Cross-Functional Partnershi