ALM Data analyst
Posted on Mar 24, 2020 by Levy Associates Ltd
ALM Data Analyst
How is it going to look like:
You work in a team of around 10 FTEs and you create ALM data sets for liquidity and interest rate risk models in the organization). The goal is correct, high-quality and timely delivery of these data sets. You are responsible for the validation by the business on accuracy and completeness of the data. You identify improvements in the process where necessary. In addition, you keep stakeholders well informed. You can easily tackle ad-hoc questions from the organization; that requires a lot of flexibility from you as a professional.
As a team you also have a role in maintaining the relationship with IT in order to get data properly disclosed and to further automate this disclosure based on self-made prototypes. The main focus is on providing functional knowledge and experience in the IT field.
The team consists of approximately 10 employees. The atmosphere within the Modeling departments is open and informal, the composition of the team is diverse and the focus is on the quantitative aspects of risk management.
To be a trusted company, it is important that we estimate our risks as well as possible. The quality of the datasets that we use for this largely determines the quality of the risk models that we build for this purpose.
To be successful in this position you have a high degree of accuracy, analytical skills and you love to work with large data sets. You like to look up the conversation to get and give clarity about the data, and you ensure that agreements are met correctly and on time.
- Academic level of work and thinking with, preferably, a degree in Econometrics, Business Administration, Computer Science or a similar degree
- Good technical knowledge (reading and writing) of SAS Enterprise Guide or SQL Or Python
- Good social skills due to. stakeholder management and the detailing and challenge of requirements
- +/- 3 years relevant work experience, preferably in the financial sector
- Knowledge of (Credit)risk management in financial institutions and/or predictive models,
- Preferably knowledge of financial instruments, such as stock trading, options, futures, swaptions, loans, financial restructuring/recovery, collateral and credit administration systems.
- Preferably knowledge of innovative data science techniques such as text mining, deep learning, unstructured (big) data collection and ditto business intelligence.