- Master's degree or PhD in computer science, mathematics/statistics, engineering, physics or other quantitative disciplines
- 2+ years of work experience in quantitative analysis/modeling at a financial institution or consulting firm
- Strong experience in working in quantitative teams focused on credit risk, with a high degree of independence and responsibility
- Proven experience in applying machine learning algorithms to credit risk modeling, such as Neural Networks, Support Vector Machines, and CART
- Deep experience with analytical software (R or SAS) and solid working knowledge of scripting languages (e.g., Python). Experience with databases (SQL or NoSQL) would be beneficial
- Skills to communicate complex ideas effectively
- Team player, with a professional and service-oriented attitude
- Ability to work effectively and collaboratively with people at all levels in an organization
- Fluent in German and English
Who You'll Work With
You’ll be based in Frankfurt and will work within the Risk Management practice as part of the Risk Advanced Analytics (RAA) group. This group is tasked with developing analytical tools in all fields of risk (strategic risk management, credit, operational, treasury, liquidity, etc.).
What You'll Do
You will be at the center of the Risk Analytics group’s analytical engine, implementing machine-learning techniques for our client projects in the area of credit risk management.
Building upon your ideas and experience, you will apply machine learning algorithms to modeling credit risk to gain new insights and translate them into distinct client contributions. You will be fully integrated into client service teams, advising clients on credit risk processes. This includes gathering and analyzing information, formulating and testing hypotheses, and developing and communicating recommendations. You will also have the opportunity to present results to client management and implement recommendations in collaboration with client team members.
You will use a broad range of internal and external sources in your work, including your access to an outstanding knowledge structure and international network of experts. You will contribute to knowledge development by helping define and expand distinctive risk-based methodologies to support top management-level strategic decisions.