The successful candidate will join the Advanced Analytics COE (Center of Expertise) of Global Risk Management Holding Area to develop our credit risk management development framework, contribute to several key projects for the re-engineering of global risk management systems necessary to assess more complex problems. Testing and development of internal tools. You will work in a multicultural environment that will allow you to develop your career in an international context and you will be part of the Data Science Community of BBV
The Global Risk Management (GRM) has a clear strategy aimed at achieving, risk adjusted profitability and recurrent value creation; using a model portfolio approach to manage the group’s activity as a basis for a better capital and a better risk management digitization framework.
GRM Analytics is the Discipline, within Global Risk Management, responsible for the development of methodologies and models in the field of risk modelling of the BBVA group. The analysis and use of new data sources and the implementation of new modelling techniques is a core activity within this team.
What you will do:
Based in Madrid, the successful candidate will join the Advanced Analytics COE (Center of Expertise) of Global Risk Management Holding Area. The main purposes of the role are:
- Develop our credit risk management development framework.
- Contribute to several key projects for the re-engineering of global risk management systems necessary to assess more complex problems.
- Testing and development of internal tools.
- You will work in a multicultural environment that will allow you to develop your career in an international context and you will be part of the Data Science Community of BBVA.
At least 3 year of experience with programming:
- Strong Python (Numpy, Pandas, Scipy and Scikit-learn are a must).
- Strong Spark (pyspark) and Ecosystem (Spark MLlib and Spark Core).
- Familiar with machine learning algorithms and mathematical optimization formulations (Linear programming, nonlinear programming, derivative-free optimization and mixed-integer programming).
- Experience coding numerical methods.
- PhD in Computational Mathematics, Operations Research or Computer Science.
- Previous experience developing machine learning/optimization libraries in Windows and Linux.
- Previous working experience in reinforcement learning.
- GitHub portfolio and/or contributions to open-source projects.
- Research experience, publications in optimization/machine learning journals.
We are looking for an enthusiastic person, who has the following characteristics:
- Exceptional problem-solving skills and strong attention to detail.
- Ability to explain complex topics in a simple and clear manner.
- Proactivity and autonomy to develop different lines of work.
- Good communication skills. Ability to clearly communicate progress to non-technical people.