Hybrid Quant Risk Analyst: Data-Driven Credit Risk

Nordea; Finland; Hybrid; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Hybrid working model

Job summary

Nordea in Finland seeks a Quantitative Risk Analyst to contribute to data analytics, optimize data flows, and support data governance across credit risk models. You will coordinate data activities and communicate effectively within Risk Models areas.

Key responsibilities include data quality root‑cause analysis, data reconciliation, and ensuring robust data lineage, with a focus on delivering accurate reports to senior management and external bodies.

Qualifications

• Experience with Credit Risk modeling, capital reporting and regulatory requirements such as BCBS239 and TRIM.
• Knowledge of SQL, SAS, Snowflake and Python or similar.
• Master’s degree in finance, economics, engineering, physics or mathematics.
• Strong focus on results, deliveries and deadlines.
• Experience with Big Data would be an asset.
• Interest in finance and technology.

Responsibilities

• Root‑cause analysis for data quality issues and data reconciliation across sources.
• Optimize data flows and algorithms to enhance data processes.
• Own and manage all data used in credit risk models across the end‑to‑end lifecycle.
• Identify and govern critical data elements with knowledge of credit risk models and regulatory expectations.
• Secure the Data Governance framework with robust Data Quality Controls and document Data Lineage.
• Prepare reports and documentation for senior management and external reporting.

Skills

Data analytics

Data governance

Communication

Analytical thinking

Attention to detail

Collaboration

Problem solving

Education

Master's degree in finance or related

Tools

SQL

SAS

Snowflake

Python

Job description

Nordea in Finland seeks a Quantitative Risk Analyst to contribute to data analytics, optimize data flows, and support data governance across credit risk models. You will coordinate data activities and communicate effectively within Risk Models areas.

Key responsibilities include data quality root‑cause analysis, data reconciliation, and ensuring robust data lineage, with a focus on delivering accurate reports to senior management and external bodies.