AVP - Strategic Fraud Analytics - BFS

AVP - Startegic fraud analytics

- Team is responsible for the engineering, industrialisation and delivery of analytical products to help mitigate financial crime in all the countries HSBC operates in.

- Working across multiple fraud risk types our work helps to keep our customers and the communities we operate in safe from financial crime.

- With a passion for all things data our innovative multi-disciplinary team works with cutting technology to deliver products that solve real business problems. Our team consists of agile, create and solution driven individuals that want to use big data, analytics and machine learning to detect and prevent financial crime.

- As an analyst you will work closely with stakeholders and other functions within WPB and WSB and Fraud to develop innovative and actionable models which help to manage fraud risk. The solutions we deliver need to manage risk effectively, be commercially focused, scalable across our many markets and lines of business and stand up to the rigors of internal and external scrutiny and review.

- Support the development and deployment of models and analytical products to manage and mitigate financial crime risk

- Develop new analytical practices in partnership with other Research & Analytics functions.

- Manage model risk through the application of controls and standards

- Interpret and manipulate large data set to extract trends and identify issues and risk

- Use data visualization techniques to present complex ideas and provide self-service analytics

Knowledge & Experience / Qualifications (For the role - not the role holder. Minimum requirements of the role.)

We're a multi-disciplinary team and value experience from a wide variety of industries and specialisms. If you have a background in analytics, big data, software engineering, devOps or data management we'd love to hear from you. We're looking for people with skills and experience in:

- Degree level educated (or relevant work experience) in Data Science, Computer Science, Statistics, Mathematics or any other quantitative discipline

- Advanced analytical techniques e.g. regression analysis, predictive analysis, data mining, machine learning

- Experience of one or more of the following: SAS, Python, Spark, SQL/HQL,R, Scala, Google Cloud Platform (GCP) , AWS.

- Domain experience in financial crime would be an advantage but not essential (Fraud, Anti Money Laundering, Terrorist Financing, Anti-Bribery & Corruption, Sanctions)

- Experience in visualization technologies such as Qlikview/sense, Tableau would be a plus

- Strong analytical thought process and aptitude for creative problem solving.

- Agile methodologies