Risk Data Analyst/ Data Scientist
Sobre a vaga
In the creation of a Risk International Hub in Lisbon, our Client is currently looking for a Risk Data Scientist with proven previous experience in Credit Risk for it’s Stress-Testing Department.
Within the department, Stress-Testing Methodologies & Models (STMM), is responsible for the S/T models and methodologies for all major drivers impacting P&L, liquidity and capital planning globally for the Bank. The team is also responsible for coordinating the development and implementation of models and methodologies that combine different risks (e.g. market risk, operational risk, credit risk, liquidity risk…). It ensures the consistency and robustness of S/T models and methodologies by developing models as a transversal expertise center for the Group liaising with the different topics experts in RISK. The team is composed of quantitative analysts and data scientists with a clear orientation towards innovation. Its transversal positioning also brings the opportunity to work with stakeholders in many regions and get an understanding of a variety of business of the bank.
The team expertises are related to:
- Quantitative analysis: portfolio modelling, rating migration and LGD models,
- Time series modelling for the anticipation of risk parameters,
- Numerical treatments
- Reporting and analysis of stress testing outcomes.
- Carrying out stress test exercise for internal and Regulatory purposes: stress test exercises rely on macroeconomic parameter forecasts. These parameters have impacts on forecasted default rates and credit risk cycle. You will be in charge of calculating and computing these parameters and then feeding the stress test engine
- Data science: treatment of large dataset in a big data environment, using Python/R code. Data analysis and treatment on both input parameters (including loan and client level portfolio data) and on model generated data.
To be successful in this role, the candidate should meet the following requirements:
- Strong academic background in a quantitative field such as mathematics, economics, finance, statistics, engineering and others, with at minimum a Master degree.
- Previous experience (2-4 years’ experience) in Credit Risk and/or in the Banking industry.
- Background in programming with ability to use at least two of the following tools: Python, R, VBA, SAS, SQL.
- Knowledge of BCBS, EBA and IFRS9 regulations/principles is a plus.
- Languages: English.