Hybrid Quantitative Data Scientist – AML Monitoring

Deutsche Bank; Greater London; Hybrid; GBP 60,000 - 80,000; Full time

Job summary

A major financial institution in the UK is seeking an experienced professional to lead Transaction Monitoring model strategies within their Group Strategic Analytics team. The role requires expertise in developing and optimizing quantitative models, with a keen focus on compliance and anti-money laundering. The ideal candidate will have a Master's or PhD in a quantitative discipline, proficiency in Python and Machine Learning, and a hands-on approach to model deployment. The organization promotes a flexible working model and a commitment to employee wellbeing.

Benefits

Hybrid Working

Competitive salary and non-contributory pension

30 days’ holiday plus bank holidays

Life Assurance and Private Healthcare

Flexible benefits including Retail Discounts and Gym benefits

CSR volunteering leave

Qualifications

• Experience developing models in financial services or Financial Crime setting.
• Hands-on model implementation in Python/Spark and cloud environments.
• Knowledge of Anti-Money Laundering (AML) transaction monitoring.

Responsibilities

• Implement and optimize transaction monitoring model framework.
• Collaborate with Compliance and Model Risk Management teams.
• Guide team members in model calibration and optimization.

Skills

Model development

Quantitative analysis

Python

Machine Learning

Data quality management

Education

Master’s or PhD in Mathematics, Computer Science, Data Science, Physics, or Statistics

Tools

Python

Spark

Cloud environments

Job description

A major financial institution in the UK is seeking an experienced professional to lead Transaction Monitoring model strategies within their Group Strategic Analytics team. The role requires expertise in developing and optimizing quantitative models, with a keen focus on compliance and anti-money laundering. The ideal candidate will have a Master's or PhD in a quantitative discipline, proficiency in Python and Machine Learning, and a hands-on approach to model deployment. The organization promotes a flexible working model and a commitment to employee wellbeing.