Jatin Garg is a Data Scientist Controller at Siemens Financial Services in Munich. After graduating in Data and Knowledge Engineering (M.Sc.) from the University of Madgeburg, he started his career in Data Science at Bayer, where he was working on various machine learning projects.
From Network Administrator to Data Scientist
As it happened, I found my passion for Data Science through a random blog post on the internet.
Initially, I wanted to become a doctor. Still, due to the university’s selective admission restrictions, this didn’t work out, and I got admitted to the computer science course after a friend informed me by chance.
My focus was on Computer Networking, in which I gained my first professional experience as a Cisco-certified network administrator. I helped the Network Architects in setting up the network infrastructure of a new site by programming Cisco devices like routers and switches.
Besides that, I was also very interested in the growing topic of 3D printing (It was in the direction of printing porcelain) and planned to start my own company in this area but missed the change to realize it.
The increasing hype and business potential sparked my interest in Data Science.
I came to Data Science via a blog post on the internet, whose author I didn’t even know at that time. It was 2014, and the post was about the increasing relevance of big data and its potential for business. It caught my attention, and I decided to contact the author who told me that he was starting a master’s degree in a data-related subject at a German university.
The master study, called Data and Knowledge Engineering, was offered by the University of Magdeburg. I enrolled in this course, which covered classic Data Science topics like recommender systems, databases, and machine learning. This was my first experience with Data Science. The curriculum was very versatile and gave the students many directions of development.
The Gap between Theory and Practice
During my studies, I gained further experience as a Data Scientist in a large DAX company. I had the opportunity to do an internship in the finance department and to write my master thesis there.
The University of Madgeburg has excellent teachers and offers many possibilities for students to grow. My studies provided me the necessary technical knowledge for developing machine learning models. However, my day-to-day work as an analyst also requires profound people skills. This is something you don’t learn from books, but it is essential for the success of such projects.
Finance👔 + Data📊
I believe that with the help of my technical knowledge, I can create significant added value in the finance area. My first job was at Bayer in the CFC (Corporate Finance Controlling) department. I developed a time series model to forecast liquidity. At first, it was a challenge to get familiar with all finance concepts due to my different background. However, I enjoyed working in this area, so I decided to develop in that direction.
Last year, I moved to Munich to work at Siemens Financial Services as a Data Scientist Controller. Apart from being responsible for country controlling and budgeting, I’m in charge of various digitalization topics in the Business Controlling department.
Create Value from Data
Don’t just look at numbers and models. Try making sense from it.
For me, the value of data is evident when incorporating solutions into existing business processes.
During my internship and my master’s thesis, I worked on liquidity planning. Before I started, the planning procedure was fairly Excel-heavy. My task was to digitize this manual process, and I used R and a time series model to predict the financial outcome. The model enabled the company to reduce risk and manage its cash more effectively, which resulted in increasing profit.
Another example of creating value from data is the automation of checks. I wrote a program in SQL to help my colleagues automate their manual tests. This lead to time savings of > 98%.
What fascinates me most about data science is the possibility to drive efficiency. Most companies still have too many tasks that can be automated with the help of Machine Learning & Co.
It increases the relevance of your job since you can save time and focus on more value-adding tasks. Therefore, I am always pleased when I can help colleagues with my technical know-how.
Advice for aspiring Data Scientists
Data Science is not an end in itself. Nor should Data Science be the sole responsibility of Data Scientists. Data Science is a skill that everyone should have to make better decisions. Of course, not everybody can program a neural network. Still, one should understand the basic principle and know when and how to use it. I believe that this is also the trend.