I do not work in one specific business area but join different teams for some months to support them in setting up a data driven solution for a specific problem. It is very interesting to dive into a different business area every few months for a new project. As the requirements of the individual areas typically vary widely so do the solutions they need. I often have to teach myself new methods or dig deeper into the understanding of methods to check whether they might be suitable for my current use case. I really enjoy that my work is not only setting up the same model for each problem and that I often need to come back to my “classical” statistics skills to investigate whether my product is working as expected – and if not why. To keep up with the changes and progress in the machine learning world, I am always motivated by my manager to spend time on learning more about topics that might help me with my job. This might be Udemy or Coursera online courses to learn more about new methodological approaches and the theory behind them or courses to improve my skills to work with the common tool set. For example I spend some time to improve my programming skills in terms of making more use of object oriented functionalities or design patterns.

I am really happy that one of the major objectives in my Data Science team is to bring solutions into production and it makes me proud to see products I built with the project team being actually used by the business colleagues as part of their daily work. On the personal side I am very proud of having adapted successfully to rapidly changing business requirements and the technical landscape in terms of tooling and ways of working. After doing research at the university it was quite hard for me to switch the setting and to cope with the fact that working in practice requires a reordering of priorities. That is I had to accept that a good solution does not have to be the best solution in theory but the one that yields useful results while being stable and reliable in a productive environment. Furthermore, with the help of my colleagues I have managed to build up a community for Data Scientists all over the company in the last years. This allows people from different areas to get into contact and exchange about what kind of projects they are working on. We organize regular events like workshops on code improvements or the use of common infrastructure and deep dive sessions on the methods used in specific projects. We also organized a Hackathon in the last year to collect ideas for the further development of on an actual Data Science project. All in all, I can say that I have developed and improved many of my personal competences since having started as a Data Scientist at TUI: I have improved my knowledge about tools and infrastructure (especially on working with cloud resources), refined my programing skills and enhanced my organizational skills when it comes to collaboration with my direct colleagues in the Data Science team but also with colleagues in specific project teams.