Research Scientist (Applied Statistics/Data Science) | Mathematiker (m/w/d)
Bundeskriminalamt
Job Summary
This role involves working as a Research Scientist in the Crime Development Think Tank of the BKA's Criminological Institute, focusing on Data Science or Applied Statistics/Computational Social Science. The successful candidate will identify and analyze crime trends, develop new data analytical methods, and contribute to evidence-based descriptions of crime development within societal changes. Key responsibilities include developing analysis strategies for socio-scientific questions, creating reports and presentations, and fostering collaborations. For Data Science, this extends to developing and programming AI-based automated analyses. For Applied Statistics, it involves developing script-based automated analyses and working with complex statistical methods. The position requires a scientific university degree in a relevant field, practical experience with statistical analysis software, and applying statistical methods to socio-scientific issues. The role is ideal for professionals eager to apply advanced analytical skills to real-world societal challenges, contributing directly to optimizing police work.
Required Skills
Education
Completed scientific university degree (Diplom, Magister, or Master) in Data Science, Computer Science, Statistics, Mathematics, Empirical Methodology, or a comparable scientific university degree
Experience
- At least 2 years of experience with statistical analysis software (Python or R) in education or professional practice
- At least 2 years of experience (in education or professional practice) in applying statistical methods to socio-scientific questions, including multivariate statistics, time series analysis, scenario, and simulation procedures
Languages
Additional
- Willingness to travel occasionally (approx. 3-5 times per year, up to one week per trip, domestic and international) for professional events and coordination with internal and external partners. Ability to handle short-term, time-critical assignments.
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