Senior Credit Risk Data Scientist

About the role

As Credit Risk Data Scientist, you will own the design, development, and deployment of data-driven credit models and automated decisioning systems for small and medium sized enterprises. This is a hands-on technical role that sits at the intersection of data science, ML engineering, and credit risk strategy. You will write production ready code, build end-to-end pipelines, and translate model outputs into real credit decisions.

What you'll do

• Build, validate, and deploy credit risk models owning the full lifecycle from feature engineering and target variable definition through to production deployment, monitoring, and recalibration.
• Design and build end-to-end data pipelines in Python and SQL, integrating internal behavioural data, open banking feeds, bureau data, and third-party sources into scalable, production ready workflows using orchestration tools such as Airflow and dbt.
• Develop, test, and iterate on automated credit decisioning logic translating model outputs into approval, decline, and limit assignment rules within our decision engine, and monitoring their performance post deployment.
• Own model deployment, versioning, monitoring, and drift detection. Building the infrastructure that keeps our models performing reliably in production using PSI, Gini, KS, and related diagnostics.
• Analyse portfolio performance, identify risk drivers, and translate empirical findings into actionable credit strategy recommendations.
• Design and build EWS frameworks that surface deteriorating credit quality early, enabling proactive portfolio management and collections prioritisation.
• Partner with Risk Management, Data, and Engineering teams to build E2E data processes together.

What you'll need

• Degree in a quantitative or engineering discipline or related field.
• 3–5 years of hands on experience in data science, ML engineering, or quantitative credit risk. Production model deployment experience is essential.
• Strong Python capability. You write clean, production ready code.
• Strong SQL skills for data extraction, feature engineering, and pipeline development.
• Direct experience building and deploying predictive models and monitoring them post-deployment.
• Good understanding of credit risk concepts for unsecured SME exposures.
• Experienced in agile development and the ability to own and drive cross functional projects.
• Fluent in English; additional European languages are a plus.

Nice to have

• Experience with pipeline orchestration tools such as Airflow or dbt.
• Experience working with APIs, decision engines, and data aggregation and orchestration services.
• Familiarity with open banking data and transaction level insights.
• Experience with cloud platforms such as GCP, AWS, or Azure and modern data infrastructure tools such as Snowflake or BigQuery.

What they offer

• Attractive remuneration
• Flat hierarchy and transparent communication in a relaxed, professional atmosphere
• Flexibility and possibility to work remotely
• Monthly mobility benefit
• Wellhub Membership
• Pliant Card with monthly credit to explore the product and enjoy food with colleagues