First of all, tell us something about yourself: Which hashtags describe you the best?
#constantlearning, #askwhy, #justdoit, #active, #nightowl
If you like: Tell us a bit more about them.
I think there are always new things to learn – especially in data science, but of course not only. There are so many interesting and exciting things in this world, so the idea of "constant learning" will always be part of my life. In addition, there are moments when you simply have to roll up your sleeves and try things out – to just do it. Of course, we must never lose sight of what it's ultimately about and always ask ourselves how meaningful that is.
The hashtag #nightowl comes from the fact that I had to work night shifts occasionally while studying. But that was also because I wanted to keep doing sports and not neglect my family and friends while I was studying and working.
Now let's talk about your professional life: What do you do at Swiss Post?
I started at Swiss Post in Bern as a data analyst in May 2021. My two colleagues and I are helping the company with its digitalization efforts by focusing on data analytics and data science. This includes some smaller analyses and reports, as well as comprehensive and complex data science projects.
What did you do previously and why did you join Swiss Post?
I’ve been with Swiss Post for over nine years now, right from when I started as a trainee after my Matura. Since then, I've been involved in some pretty exciting work – doing some consulting within the company but also creating and developing specific logistics solutions. The thing I've always liked about Swiss Post is that as large company it can offer you so many opportunities to develop. Moreover, it represents values that are particularly important to me, such as quality and reliability.
Tell us about the most exciting thing in your job.
My job has a lot of variety that keeps me on my toes. I especially like having the chance to discuss things with colleagues from different disciplines. Achieving good project results always requires people with different backgrounds to work together closely. In addition, diversity always gives you a new perspective on things, which often is inspiring and keeps us from getting stuck in pigeon-hole thinking.
Which data science skills are especially in demand in your job?
Understanding the technology is of course extremely important. But one of the key things I've learned so far is that as a data scientist and analyst there are always things you don't know – which means you always have to keep learning. In other words, you need to be able to think on your feet and know how to get information quickly and easily.
Soft skills are also very much in demand. By this I mean, above all, being able to deal with people and explain complex models and ideas so that anyone can understand them and that they make sense in everyday life.
Do you think of yourself more as a techie or as an analyst? Or as a creative genius, management superhero or generalist wizard?
I don't think of myself as a techie. My background in business administration puts me closer to a generalist, one with a creative streak when it comes to analyzing things. I like looking at problems from different angles and finding solid, creative solutions that work for everyone.
What do you remember the most when you look back at your time in the MSc in Applied Information and Data Science program?
My first programming success was a small breakthrough for me of which I was immensely proud – even though it was only a very simple task ;-).
What are the biggest challenges in your job at the moment?
Everyone is talking about data topics at the moment, and there is an incredible amount of interest in the field, with very high expectations. However, the levels of knowledge among individuals vary hugely. That's why I think it's important to create a common understanding and knowledge base as a starting point for projects and then to manage expectations accordingly. That's a very tall order, one that also requires a lot of soft skills.
The question of how to prioritize projects also remains a big challenge. There are often a lot of requests and questions about projects, so I need to ask myself how do I choose the right ones and apply procedures that allow me to make objective and fair evaluations?
The topic of data science itself is one of the biggest challenges because it offers so many ways to solve a problem. That makes it hard to choose the right approach, something that you can't learn on the fly and calls for a lot of experience. It also means being able to adjust the expectations you have on oneself. As I said before, it's impossible to know everything.
What advice would you have for others starting in the same job?
Don't give up – if you chose the program as a non-techie, you might be a bit overwhelmed at first. But that's all the more reason to hang in there. It's definitely worth it!
And finally: What new hashtag are you aiming for in 2021?
#recommendersystem. In 2021, I plan to focus more on recommendation algorithms and their use in practice.
Many thanks to Michèle Odermatt for this inspiring interview and these very interesting insights into her everyday working life.
