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languages and statistical software such as Python, R, Stata or SAS; Knowledge of quantitative modeling of financial markets, econometric techniques, machine learning, or quantitative empirical research
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or agile approaches. You are confident in qualitative and quantitative research methods, data analysis techniques and inferential statistics. It is an advantage if you are familiar with the process of
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, interests and background: A MSc degree in health technology assessment, health economics, health sciences, econometrics, economics, statistics, operations research, computer science, applied mathematics or a
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, Sklearn, Conda, Github, etc.). Experience with natural language processing, especially LLM is a plus; Strong mathematical background, in particular statistics & probability, linear algebra, calculus and
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statistics & probability, linear algebra, calculus, optimization, and Markov decision processes. You have knowledge of financial engineering concepts such as credit risk assessment and mitigation, probability