Senior ML Engineer - Financial Crime Risk (Remote EU)

Finom; United States; Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Stock options program

Work & Swim Cyprus program

Remote/hybrid across Europe

Job summary

Finom is seeking a Senior Machine Learning Engineer to own end‑to‑end ML development for fraud detection and onboarding. You’ll deploy, serve, and monitor models in production on a small, fast‑moving team, driving improvements across all markets.

You will work on scalable ML infrastructure, champion‑challenger evaluations, and integration with API services. English proficiency and a strong quantitative background are required, with EU travel and hybrid work across Europe.

Qualifications

• 5+ years as a Machine Learning Engineer or Data Scientist with production deployment experience.
• Strong in SQL and Python; experience with Databricks.
• Architectural skills to design and improve infrastructure for models.
• Experience with model monitoring and champion–challenger / A/B evaluation in production.
• Fluent English and quantitative education.

Responsibilities

• Full‑stack ML model development (binary classification and regression) for financial crime prevention during transactions and onboarding.
• Own and evolve the Dynamic Risk Score (DRS) across all markets with daily client base scoring.
• Handle a portfolio of transaction rules with ML‑driven detection and alerts.
• Set up continuous monitoring of model and rule performance in production.
• Participate in infrastructure development for ML model operationalization and inter‑service interactions.

Skills

SQL

Python

Production deployment

FastAPI

Model monitoring

Education

Bachelor's degree in a quantitative field

Tools

Databricks

FastAPI

Job description

Finom is seeking a Senior Machine Learning Engineer to own end‑to‑end ML development for fraud detection and onboarding. You’ll deploy, serve, and monitor models in production on a small, fast‑moving team, driving improvements across all markets.

You will work on scalable ML infrastructure, champion‑challenger evaluations, and integration with API services. English proficiency and a strong quantitative background are required, with EU travel and hybrid work across Europe.