Data science jobs in Banking industry

Data Science in Banking

Banks are among the largest employers of data scientists globally. Credit risk, fraud prevention, anti-money laundering, algorithmic trading, and customer analytics are all driven by sophisticated ML and statistical models.

Common roles

Credit risk data scientists, AML model developers, fraud detection engineers, quantitative analysts, and customer value modellers are in high demand across retail, commercial, and investment banking.

Key skills

Python, R, SQL, logistic regression, gradient boosting, network analysis for AML, NLP for document processing, and model risk management (MRM) frameworks are essential in banking data science.