Time left to apply End Date: December 17, 2024 (3 days left to apply)
Job requisition id JR00055510
Excited to grow your career? BBVA is a global company with more than 160 years of history that operates in more than 25 countries where we serve more than 80 million customers. We are more than 121,000 professionals working in multidisciplinary teams with profiles as diverse as financiers, legal experts, data scientists, developers, engineers, and designers.
About the job: We are looking for a Ssr. Risk Data Scientist to join the IFRS9/CORE team. We seek an experienced, proactive, and empathetic profile with leadership and autonomy in decision-making, as we participate in regulatory initiatives and projects.
You will be part of the IFRS9/CORE team and perform the following main tasks:
Collaborate with other areas on projects to identify improvement opportunities in processes related to default definition and in areas that utilize information from default databases. Implement methodologies and calibrate parameters for provisions and capital. Conduct and monitor backtesting of parameters. Actively participate in the NGA EBA project, which includes the development and adaptation of the default definition. Participate in internal and external audits, as well as inspection processes. Monitor open lines within the GRM Data & Advanced Analytics area with internal and external control agencies. Communicate and resolve questions with different stakeholders. Review tasks developed within the team to optimize them through AI. What are we looking for? Current student or graduate in related fields such as Actuary, Economics, Mathematics, Statistics, Industrial Engineering, and/or Systems. Leadership skills. At least 3 years of experience in similar positions. Knowledge and experience in risk management and/or parameters under IFRS9 regulations. Useful tools: Proficiency in Statistics, Machine Learning, Python, Spark, and SQL. Intermediate/Advanced English. Skills: Capital calculations for market risk, Interest Rate Models, IRB, Model risk, Probability of Default, Risk data model (taxonomy and data governance), Scoring Models.
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