Credit Risk Data Scientist: IFRS9 & Cloud Modeling

NOBA Bank Group; Norway; On-site; NOK 700,000 - 1,000,000; Full time

Job summary

NOBA Bank Group in Norway seeks a Data Scientist to develop credit risk models and maintain the engine that puts them into production. You will work across modelling, engineering, and analysis, moving from data to decision.

You will implement models in production code, write tests, monitor risk, and collaborate with IT, Finance, and other departments, contributing to the move to Databricks on Azure.

Qualifications

• Higher education in a quantitative subject.
• Around 2–3 years of relevant working experience (PhD or research counts).
• Solid Python for data work: pandas, numpy, scipy.
• Confidence writing your own SQL, or ready to learn quickly.
• Habit of maintainable code with version control, tests, and code reviews.
• Strong written and spoken English for presenting to specialists and non-specialists.

Responsibilities

• Develop, validate, implement, and simulate credit risk models (PD, LGD, EAD, staging, macroeconomic adjust).
• Implement models as production code in the ECL engine with unit tests and code reviews.
• Build and maintain risk monitoring, forecasting, and reporting tools.
• Document models and implementations to validators and regulators.
• Query and prepare data (mostly SQL).
• Assist move to Databricks on Azure.
• Present analytical results to technical and business audiences.
• Collaborate with IT, Finance, Marketing and other departments.

Skills

Python

SQL

Pandas

Numpy

Scipy

Version control

Testing

Documentation

English communication

Education

Quantitative degree

Tools

Databricks

Azure

Job description

NOBA Bank Group in Norway seeks a Data Scientist to develop credit risk models and maintain the engine that puts them into production. You will work across modelling, engineering, and analysis, moving from data to decision.

You will implement models in production code, write tests, monitor risk, and collaborate with IT, Finance, and other departments, contributing to the move to Databricks on Azure.