Financial Crime Data Services Analyst

Financial Crime Data Services Analyst

Join to apply for the Financial Crime Data Services Analyst role at Yorkshire Building Society.

This range is provided by Yorkshire Building Society. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

We’re looking for a dedicated and knowledgeable Financial Crime Data Specialist to join our team in Bradford. This role is pivotal as part of our Financial Crime analytical capabilities, through applying quantitative techniques to support the definition and development of Fraud & Financial Crime controls and solutions. The ideal candidate will be a key resource in identifying, breaking down and communicating complex data and data driven solutions, supporting business change, and qualifying & mitigating risks.

This role will be based in our Bradford based Head Office, with flexible working from home capability.

• Production of 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
• 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 calibrated and optimised tuning of financial crime solutions
• 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.

• A proactive mindset with a passion for delivering & controlling data driven solutions
• Analytical & problem-solving skills, strong data analysis, investigative, and risk assessment capabilities
• Ability to carry out quantitative analytics and model deployment
• Experience SAS / SQL / Python or R for dealing with complex data sets / large sets of data
• Utilise best practice statistical, analytical, and modelling techniques to perform complex analysis of the Financial Crime Risks, to enable the calibration & optimised tuning of financial crime technologies would be desirable
• Effective communication, engagement skills and the management of internal and external stakeholder relationships
• An understanding of the financial services industry, markets and competitors is a bonus (not essential)
• Knowledge of relevant legislation, compliance, corporate governance and risk management (not essential).

At YBS we don’t have shareholders, we have members. We care about people and that includes you. So here, you’ll be respected for who you are, you’ll be able to bring your whole self to work, and you’ll have everything you need to build a long and rewarding career.

We offer a range of flexible working options without unsocial hours, which can help you find a healthy work-life balance. Whether it’s finishing early for an appointment or fitting your studies and hobbies in around your working hours, we can help make it work for you. We’re happy to talk flexible working and you can reach out to us anytime to discuss this during the recruitment process.

We want you to feel challenged here. Whatever your ambitions, we’re committed to helping you develop your skills and move ahead in your career journey. From day one, we’ll set you up for success with a brilliant induction and full training plan, all designed to get you up to speed quickly.

We’re passionate about creating an inclusive environment where everyone is welcome and able to be themselves. If…