ML/Data Engineer (Credit Assessment)

This role sits within the credit assessment analytics organization, which uses data, AI and machine learning to improve decision-making in credit screening and collections. The position connects data science with operational use, turning analytical models into reliable systems used in the business.

The engineer will build data pipelines and data models in DWH environments such as BigQuery, integrate credit-scoring and other machine-learning models into production, and create inference and MLOps processes. Responsibilities include monitoring model accuracy, automating retraining and operations, optimizing data processing performance, and coordinating requirements with data scientists and business teams. The technical environment names GCP, BigQuery, AWS, DataRobot, Python and SQL.

Applicants should have around three or more years of practical experience in data engineering, ML engineering or a related area. The required background includes development with SQL and Python, experience building or operating cloud-based data platforms such as GCP or AWS, or practical experience deploying and operating machine-learning models in production.