Financial Crime Lead Data Analyst

Adway Associates; Bradford; On-site; GBP 45,000 - 65,000; Full time

Job summary

A leading consultancy in financial services located in Bradford is seeking an expert in financial crime risk management. This role involves advanced data analysis and reporting, requiring proficiency in Python or R, and experience in statistical modeling and analytical techniques. The ideal candidate must possess strong problem-solving skills and the ability to communicate complex data insights effectively. Additional knowledge of financial crime regulations and tools is desirable, making this position critical for ensuring compliance and risk mitigation.

Qualifications

• Experience using Python or R for data manipulation.
• Ability to carry out statistical analytics and model deployment.
• Excellent knowledge of supervised and unsupervised machine learning techniques.

Responsibilities

• Lead complex analysis across multiple risk types like AML and Fraud.
• Communicate results of analysis clearly, making risk mitigation recommendations.
• Develop the financial crime function with specialist data training.

Skills

Problem solving

Data manipulation

Statistical analytics

ETL processes

Machine learning techniques

Excellent communication

Education

Related financial crime qualifications

Tools

Python

R

SAS

SQL

Tableau

Power BI

Job description

Support the Groups Financial Crime Risk Management Framework & broader Society by undertaking a combination of advanced data mining, advanced statistical analytics and financial crime risk model development.

Be the data expert within financial crime, share knowledge and the develop the team, function and wider business.

The Role:

• A subject matter expert in the preparation of data from statistical analysis, modelling and predictive analytics
• 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
• Working across multiple risk types i.e. AML and Fraud, lead complex analysis and own the production of timely and accurate Financial Crime management information, trends and insights
• Identify opportunities and best practise of data management across all areas of financial crime and the wider business
• Maintain 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
• Work closely with first and second lines of control and governance to ensure that systems and processes are in line with expectations and addressing the risk using a risk-based approach
• 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
• Be viewed as an expert in the Financial Crime systems and able to process, manipulate and interpret that data to support the above accountabilities
• Communicate results of analysis to a high standard, both written and verbally, making recommendations for risk mitigation within risk appetite
• Responsible for the development of the financial crime function in relation to specialist data training, techniques and analytical 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 ETL processes, using of Python or R to enable data manipulation for modelling
• Ability to carry out statistical analytics and model deployment
• Experience SAS / SQL for dealing with complex data sets / large sets of data
• Presenting MI using Tableau or Power BI
• Excellent knowledge of supervised and unsupervised machine learning techniques
• 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