Job Openings Financial Crime Data Services Analyst

About the job Financial Crime Data Services Analyst

Support the Groups Financial Crime Risk Management Framework & broader Society by supporting the requirements for advanced data mining, statistical analytics and financial crime risk model development.

The Role:

  • Lead the production of statistical analysis, modelling and predictive analytics including supervised and unsupervised techniques
  • The ability to identify and communicate often complex data and analytical solutions to the wider department, ensuring transfer of key findings to inform business changes and risk mitigation
  • Support the Financial Crime management information analysts with smarter trend, insight and complex analysis
  • Establish a knowledge of financial crime systems and controls used by the Group, allowing for continuous improved performance
  • Use data driven reporting benchmarking across sites to identify best practice and outliers, identifying opportunities for efficiency, forecasting, service, scalability, reducing cost and risk
  • Utilise best practice statistical, analytical and modelling techniques to perform complex analysis of the Groups Financial Crime Risks, to enable proactive prevention and detection, optimised tuning of financial crime technologies
  • Communicate results of analysis to a high standard, both written and verbally, making recommendations for risk mitigation within risk appetite
  • Support the development of the financial crime function in relation to specialist data training, techniques and analytics modelling

The Candidate:

  • Ability to apply themselves to problem solving and analysing situations, delivering practical and compliant financial crime controls/solutions
  • Evidence of professional learning and development to build and maintain skills and expertise
  • Excellent knowledge of supervised and unsupervised modelling technique, using of Python or R
  • Ability to carry out statistical analytics and model deployment
  • Experience SAS / SQL for dealing with complex data sets / large sets of data
  • Able to showcase feature engineering and importance

Desirable:

  • Related financial crime qualifications.
  • Subject matter expertise in financial crime risk including experience of AML, KYC, Sanctions and Fraud retail banking products and the UK regulatory environment