Data Scientist - Credit Risk Modelling (Remote)

ABN AMRO Bank N.V.; Amsterdam; On-site; EUR 70,000 - 95,000; Full time

Benefits offered by this job

Competitive salary (36-hour week)

Excellent pension scheme

Work-from-home flexibility

Five weeks vacation + 2 mandatory days

Development budget €1,000/year

Annual public transport pass

Job summary

ABN AMRO's Non-Retail Credit Risk Modelling team is redeveloping credit risk models for lending portfolios. As a medior Data Scientist you contribute across the model lifecycle—from initiation to internal validation and regulatory inspections—collaborating with stakeholders bank-wide.

You bring a quantitative background, 2+ years in credit risk modelling, fluent English, and coding in Spark, Python and SQL.

Qualifications

• Master's or PhD in a quantitative field.
• >2 years’ experience in a credit risk modelling team (IRB/IFRS9/stress testing).
• Fluent in English (speaking and writing).
• Strong communication with stakeholders at all levels.
• Proficient with Spark, Python and SQL; familiarity with Azure DevOps / Git.
• Deep knowledge of banking and credit risk.
• Knowledge of Basel/CRR and regulatory trends in credit risk modelling.
• Experience with DNB/ECB communications and on-site inspections.

Responsibilities

• Redevelop credit risk models for lending portfolios.
• Contribute to the full model redevelopment cycle from initiation to validation and inspections.
• Collaborate with stakeholders across ABN AMRO to ensure model relevancy and regulatory alignment.

Skills

Strong communication

English fluency

Stakeholder engagement

Banking & credit risk knowledge

Regulatory knowledge

DNB/ECB experience

Education

Master's degree or PhD in a quantitative field

Tools

Spark

Python

SQL

Azure DevOps / Git

Job description

ABN AMRO's Non-Retail Credit Risk Modelling team is redeveloping credit risk models for lending portfolios. As a medior Data Scientist you contribute across the model lifecycle—from initiation to internal validation and regulatory inspections—collaborating with stakeholders bank-wide.

You bring a quantitative background, 2+ years in credit risk modelling, fluent English, and coding in Spark, Python and SQL.