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Student Support Staff (part time) - Risk Methods and Analytics (m/f)


The department Risk Methods and Analytics uses statistical and mathematical methods to develop predictive models used primarily in credit risk management. Team members have diverse numerical backgrounds ranging from mathematics and statistics to business with a strong quantitative focus. Typical applications are development of credit rating models, macro-economic stress testing models or economic capital models. Additionally, we use our predictive modelling competence on a number of partly big datasets to develop decision models for a wider range of business problems. Key success factors in our modelling projects are analytic skills, fast understanding of the business context and project execution. 

Your tasks:
  • Support model development projects embedded in a team of quants
  • Work with big datasets
  • Develop sound understanding of relevant business areas
  • Write code implementing analytical models in high level programming languages such as SAS, R, Python, Matlab
  • Write clear and concise documentation of models

Your qualifications:
  • Enrolled in a university program in mathematics, statistics, econometrics or business/economics with strong quantitative focus (Master, PhD)
  • Interest in predictive modelling and machine learning
  • Experience with handling of big data sets is a plus
  • Programming skills in at least one high level programming language like SAS, R, Python, Matlab and willingness to learn more. Know-how in R Shiny, SAS, SQL and low level programming languages appreciated.
  • Self-motivated, focused, result-oriented, resilient
  • Fluent English language skills; German skills are a plus

Your benefits:
  • Join our dynamic and motivated team in one of the leading banking groups in Austria and
  • Central and Eastern Europe
  • EUR 1.777,41,- monthly gross salary (on full-time basis)
  • Work-Life balance due to variable working hours
  • State of the art learning and development opportunities
We are looking forward to receiving your
online application!

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