Our AI specialist Fidel Gil had the opportunity to talk to the industry blog BASICthinking and present what he does on a daily basis for our MarTech company and what is particularly important to him.
The German interview appeared on BASICthinking on November 3, 2022.
Here you can read the complete interview in English and learn more about the profile of a Specialist AI & Data Science:
Fidel, you work as an AI & Data Science Specialist at the MarTech company matelso. Please describe to us in four sentences how you explain your job to new friends?
Sometimes that’s a real challenge, because I always have to elaborate a bit. To real laypeople, I always start by explaining what we do as a company: The MarTech company matelso develops software solutions for marketing departments in companies – but also for other areas such as sales or service. This includes our renowned Call Tracking technology, which helps marketers learn more about their customers and make their online marketing strategies more efficient. But above all, our new product, the matelso platform, with which we bring together (video) telephony, chat, mail and other direct communication channels between companies and their customers in one place. This data then enables better and more value-added business decisions.
Only then do I get to talk about my job: As an AI expert, I build applications for our platform that analyze the collected data in an automated way and identify opportunities in real time to improve the customer experience. So I make our system smarter.
What does a normal day as an AI & Data Science Specialist look like?
Alongside this, we also run the standards that every IT professional is familiar with: daily discussions and meetings in which we distribute goals and tasks and compare the results of the previous day. These are always a source of inspiration for us, as we exchange ideas and experiences here.
And what do you start the day with?
What tasks fall under your purview?
Another important aspect of my job is to continuously demonstrate and evaluate the feasibility of our projects and potential risks in order to identify them as early as possible and to define and develop realistic solutions. In doing so, it is important to always keep an eye on the big picture and to keep all aspects of the platform in mind – all functionalities and elements must always mesh seamlessly. It’s simply not a good feeling when you design something that then disappears into a drawer again for technical, logical or computational reasons – or catches digital dust on your hard drive.
How do you personally define and interpret your job as an AI & Data Science Specialist?
Only then does my other expertise come into play: knowing what specific conclusions can be drawn from various data and how to make them actionable for our customers – in other words, analyzing and evaluating how they can be used to create value. There are a number of tools that can be used for this: graphics that visualize trends that are difficult to recognize, or models that can be used to classify objects or make predictions about future events. So it’s exciting what I deal with on a daily basis.
How does your job fit into the corporate structure? That is, who do you report to and who do you work with?
Of course, the role of AI & Data Science Specialist is interpreted differently in each company. Which perspectives do you miss out on that are basically part of the job description?
What do you enjoy most about your job?
And the look on my colleagues’ faces as they watch in amazement and try out how the machines I’ve programmed do exactly what they’re supposed to, live and in color, gives me a great sense of pride in my work. It’s really indescribable.
What are you most grateful for?
In the digital industry in particular, there is often no longer a traditional apprenticeship. How did you get your job?
What advice would you give to a newcomer or interested career changer who also wants to become an AI & Data Science Specialist?
But the most important thing is: patience! And: practice, practice, practice! Much of the interaction between data, algorithms and models only becomes clear when you have run through the processes several times – not to say umpteen times. But that is an experience you must make. So, get on it!
