Data scientists are a hot commodity at the moment. They usually come from the field of physics, like Dr. Michael Milnik, and as a result have excellent mathematical skills. What other skills they should bring to the table and why being a data scientist is one of the most rewarding career choices in the world, explains the team leader of customer analysis in a discussion.
Summarize data in predictions: Data Scientists are highly demanded
Dr. Milnik, many companies are increasingly hiring data scientists, if they can find one on this scarce market. What's so interesting about a career as a data scientist?
I enjoy solving problems. The trickier, meaning the more complex the problem is, the better. Data scientists live for the challenge. And as you already mentioned, data scientists are in demand. If you are good, you don't have to worry about your future.
The reason for the increase in demand for data scientists is the enormous data growth. What qualifications will be required in the future in order to analyze Big Data?
I am currently the team leader and responsible for fifteen analysts. They all learned something important before they started their career at Blue Yonder: Perseverance. Many of us come from the world of experimental physics and we know that not everything works on the first try. You have to bite through several attempts, failures, and continuously invent new ideas to solve a concrete problem. It's very similar in the business world: Organizations have a particular issue or need for optimization and the data scientist has to find a way to the answer.
So you need endurance. Would you describe your field of work as an isolated one?
When I was a Ph.D candidate in experimental particle physics at the Universität Karlsruhe, I worked together with many physicists on a big project. That's different at a software manufacturer: We work together in smaller teams and I pay attention that each employee is assigned tasks that compliment his or her strengths. The spectrum is wide: The sales oriented colleagues define which data we need for a particular forecast in order to resolve a particular issue. They work closely together with the customer and exchange ideas. Others concentrate on the technical aspects of data quality and data integration. Yet another group develops models for standardized techniques to answer specific questions and make sure that the customer has 24/7 access to their analyses.