Why did you develop an interest in the tech sector? Did others inspire you to take this path?
Back when I was still in school, I used to really enjoy natural science subjects and I liked to occupy myself with technical gadgets and gimmicks. Given the fact that I grew up in the Ruhr region, I also got to know old industrial buildings as cultural sites at an early age. I have always been impressed by large machines, which is why I studied mechanical engineering. One of my tutors encouraged me to focus on programming and later also helped me to find my first job. My then boss also encouraged me ultimately to concentrate on programming independently of my mechanical engineering studies. He also supervised me later on when I wrote my final thesis papers in conjunction with SMS group.
How did you become a data scientist and how did you manage to gain a foothold in this industry?
When I graduated from secondary school, I didn't even know that there was such a career as data scientist. I decided to study mechanical engineering and, during an IT course in the second semester, I discovered that I really enjoyed programming. I had the good fortune of effectively having private tutorials, as I was the only student attending them at that time. The tutor was about my age and only two semesters ahead of me. The tutorial was so much fun that I started giving tutorials myself the following year. The teaching and course management experience helped me to quickly gain and expand an in-depth knowledge of programming. In cooperation with the then academic chair and SMS group, I had my first contact with the area of data science during my Bachelor's thesis. I was very interested in the subject and so, in addition to my Master's degree, I decided to continue working as a student trainee at SMS digital and to expand my knowledge in that direction. Once I had finished my studies, I had the opportunity to start straight away as a data scientist thanks to my experience. I am still learning new things every day. I think there are many options for becoming a data scientist. As you can see from me, it does not always have to be clear cut from the very beginning. For example, in the steel industry especially, my background knowledge of mechanical engineering helps me in a lot of areas. Because data scientists often have to combine process knowledge with knowledge of data analysis and programming.
What do you like about your work as a data scientist?
I really enjoy working as a data scientist, as it connects programming and logical thinking with a kind of detective work. I receive data and a related question that I then try to answer based on those data. Here, I have to study the data thoroughly and pay attention to details. If, following extensive investigations, I find the correlations that help me to answer the original question, it gives me a real feeling of success. I am always surprised at how much data can say about processes.
What is a normal working day in the life of a data scientist?
My day usually starts at 6 a.m. Among data scientists, though, I'm more of an exception in this respect. Most start their day later. I personally like to start my day early and that fits in really well with the time zone in which some members of my team work. They are currently in India and the time difference is three and a half hours. The first thing I do in the morning is look at what I have to do that day. I usually then divide up my time so that everything is as manageable as possible. My tasks are very varied and cover areas like planning, programming, reviews, data analysis, consultations, further training, and presentations. Every day at 10:30 a.m. our team gets together, at the moment there are 10 of us, and we discuss the tasks for the day ahead. Team members include software developers, managers, data scientists, and metallurgy experts. Sometimes we all work alone and sometimes we work in small groups of two or more. This ensures there is a lot of variety. Depending on the tasks in question, every day is a little bit different. My computer and a sufficient supply of coffee are constant companions.
Does data science play any role in your free time?
Yes. I like to read blogs on exciting projects and every now and again I will take courses that allow me to develop further both professionally and personally. As a data scientist, I often see things differently in my everyday life. At the moment, for example, many supermarkets and discount stores use digital loyalty cards for their customers and I often think about what potential there is in the data collected with them.
Is the tech sector a job for you or is it a vocation too?
A bit of both. I really enjoy my work and I could not imagine life without it. Even in my spare time, I often think about data science-related topics. However, I think it is important to take a step back from time to time in order to gain a clear perspective again.
What skills does a data scientist need to have?
In my opinion, the most important skills are logical thinking, structured working, patience, and a thirst for knowledge. The first two come automatically from your day-to-day tasks at work. It is also very important to be patient, as many different possibilities have to be tried out and the calculations may take a while, depending on the volume of data. A thirst for knowledge and the willingness to learn new things all the time is particularly important in the field of data science, as it is a very fast-moving industry and new methods, models, and tools are constantly being developed.
What tips or advice would you give others who are considering a career in data science?
At least if you're not scared of numbers and programming. Grab a computer and try data science out to see if it is indeed something for you. There are a large number of online tutorials, projects, and even competitions in which data are made available. This can provide you with a good insight into what it means to be a data scientist.
I would particularly encourage young women who are interested to take up this diversified profession with great future prospects. In technical lines of work, such as this one in particular, women are still under-represented. A data scientist can expect varied tasks, an insight into many different processes, and frequently working in international teams, all of which offers you an array of different perspectives and opportunities in your career.