-job description
job summary: financial crime (fc) focuses on the specific financial crime threats the firm faces now and in the future, pioneering the techniques and technology that protect our business, our customers, and the many communities in which we operate from the harms associated with financial crime. Fc harnesses intelligence, analytics, technology, investigation, information sharing, and public-private partnership to achieve this end, always seeking the most effective and efficient means. Fc is also partnering with other areas in compliance to build the case for a more efficient and effective regulatory approach by defining a potential new regulatory landscape based on practical, tested innovation and serving as a thought leader in the ongoing public debate on the future of regulatory compliance.
- the risk analytics and modelling role will develop industry-leading analytical work focused on provide cutting-edge analytic support to intelligence and investigations while pioneering techniques for discovering and targeting actual financial crime risk. The nature of the role requires intermediate - advance mathematical and programming background oriented to data analysis.
- main responsabilities:
- impact on the business
to conduct quantitative analysts in region.
- to drive experimental and bespoke analytic approaches, providing support to ongoing investigative and analytic activities and serving as an in-house developmental lab for new possible improvements across the estate of financial crime control systems.
- proactively identify key emerging financial crime threats across all fc analysis categories and interface with other regional functional areas.
- ensure clear visibility and oversight of experimental approaches, complete with expected delivery and value.
requirements
knowledge & experience
- intermediate - advance mathematical background,
- desirable knowledge on basic data science,
- python programming focused on data analysis, highly required.
- strong data visualization and communication skills.
- familiar with technological tools used in big data (hadoop, spark, bigquery).
- knowledge in analytics methodologies for ai/ml, supervised learning, unsupervised learning, llms, etc.
- basic understanding of the harms associated with the threat of financial crime.
- ease to adopt change and develop in an environment focused on innovation and generation of new ideas.
- excellent communication and inter-personal skills.
- problem solving ability.