Working abroad was a huge step, working at sea was an even bigger one. I had moments at the start, where I thought I would never pass my STCW course (sea safety, fire fighting etc, a course that is required to work at sea) I ended up having to do the course 3 times throughout my career as it has to be renewed every 5 years. I never thought at the start of my career at sea that I would become an actual uniform wearing officer. What I mean by this is an officer in a white uniform with gold stripes (Epaulettes) on my shoulders - but I did when I became the Front Office manager onboard Thomson Spirit, what a massive learning curve I did it!

Look out for experienced colleagues who can provide you with an overview of the business unit you are assigned to. Try and develop a critical mindset keeping the business in mind for every decision you take. Avoid working in silos.

I think you should be aware that communication and presentation skills, motivation and the will to grow and further develop your abilities are not only buzzwords but skills that are often underestimated but quite relevant for the work of a Data Scientist. I often have to explain and present the approaches I used and the results I obtained on various levels of granularity to different audiences. For example, when discussing with other data scientists in the company I need to be able to explain how a method works in detail. In contrast to that I have to focus on the business implications of my modeling choices when talking to the business colleagues or to managers. For the latter audience it is usually more relevant to understand the general idea of an approach rather than the details of how it is working. It is not easy to switch between these settings and to find a proper way of communicating to each of these different kinds of audiences but keep in mind that this will be a relevant part of your work as a Data Scientist. Additionally, lifelong learning is not only a buzzword at TUI. You should be motivated to actually stay up to date with the current state of the art Machine Learning procedures and bring motivation and initiative to continue your learning process. As a team, we are deeply involved in decisions that concern the way we work and the tools we use. Therefore, we have to keep track of new developments in the world of Data Science and Machine Learning and need to be willing to adopt new methods or tools if we expect them to be useful for our daily work.