- Full-stack ML model development (binary classification and regression) for the detection and prevention of financial crime during transaction processing and client onboarding: gathering requirements, prototyping, and deployment in a production environment
- Own and evolve the Dynamic Risk Score (DRS) across all markets — daily scoring of the full client base that feeds decisioning across the client lifecycle
- Handle a portfolio of transaction rules with ML-driven detection inside, optimizing their performance and the number of generated alerts
- Set up continuous monitoring of model and rule performance in production, and develop the champion-challenger framework so the best-performing model is always the one live in production
- Participate in infrastructure development for ML model operationalization and inter-service interactions, so models of varying complexity can be served and inferred





Experience with model monitoring and champion-challenger / A-B evaluation in production. Exposure to graph modeling or foundation models/embeddings is a plusA quantitative education (math, engineering, economics, or CS) and full working proficiency in EnglishCritical, analytical thinker — proactive, result-driven, comfortable working with little supervision in a start-up settingStrong in SQL and Python, and with cloud data platforms (e.g. Databricks)Strong architectural skills — able to design and improve infrastructure that serves models of varying complexityAt least 5 years as a Machine Learning Engineer or Full-stack Data Scientist, with production deployment experience (incl. API services like FastAPI)

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📌 Senior Machine Learning Engineer (Madrid)
🏢 Finom
📍 Madrid