If you are in the early part of your career, my advice for you would be to keep your options much wider. You will be in a better place to identify your potential as you progress in your career, much similar to a practical experience as compared to the textbook knowledge. If you are already an experienced professional then my advice would be somewhat different. You should go after the job that doesn't feel like a job but something you love doing (... and then you never have to work again, as the quote goes). One of the important factors of loving what you do is to continuously learn new things because learning expands your limit of thinking and then the ability to organically take up new challenges. My other advice to anyone on the success path is to believe in self-potential. It is possible to achieve anything but only as long as we believe in our efforts and keep trying. If we don't fail then we didn't try, so it is perfectly fine to fail so long as we learn from failures.

In my world, it's pretty simple. What a tour operator wants to achieve is to sell trips with a quality that results in satisfied and returning customers. Regardless of what role you have within the organization, this must be top of mind in all the work that is done. Within E-commerce, we do this by working data-driven, listening to the customer and always working proactively to deliver what the customer requests. A key to succeeding in this is to always try to see the situation from the customer's point of view.

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.