Senior Machine Learning Engineer

Finom; Madrid; Presencial; EUR 70.000 - 110.000; Jornada completa

Descripción de la vacante

Finom is seeking a highly skilled Machine Learning Engineer / Full-stack Data Scientist to join its Madrid team, developing end-to-end ML solutions for financial crime detection and onboarding processes. You will deploy models in production and collaborate across product and infra teams.

You will own the Dynamic Risk Score across markets, optimize ML-driven transaction rules, and contribute to scalable infrastructure for model serving and monitoring using SQL, Python and Databricks.

Formación

• Quantitative education in math, engineering, economics or CS.
• Fluent working proficiency in English.
• 5+ years as a Machine Learning Engineer or Full-stack Data Scientist with production deployment experience (incl. API services like FastAPI).
• Strong SQL and Python, with cloud data platforms (e.g. Databricks).
• Strong architectural skills—designing and improving infrastructure for models of varying complexity.

Responsabilidades

• Full-stack ML model development for detection and prevention of financial crime during transaction processing and client onboarding.
• 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.

Conocimientos

Machine learning

Python

SQL

Databricks

FastAPI

APIs

Cloud platforms

Architectural skills

Model monitoring

Champion-Challenger

Educación

Quantitative education (math/engineering/economics/CS)

Herramientas

Databricks

Descripción del empleo

• 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)