Written by:  

Annekoos Schaap

Young Professional Program: kick off your career with a head start

Putting your skills to practice

Our first challenge as a YPP team was to work on ‘the knowledge app’: a tool that creates an overview of the skill sets of all Itility co-workers based on company data. By extracting text from a collection of personal profiles, we aimed to predict people’s skill set as accurately as possible. After processing the data by text-mining and filtering, a clustering algorithm determined the expertise level for each skill: beginner, intermediate, or expert. Users were then given the option to perform a quality check on their given skill levels to improve the model.

The knowledge app project gave us a taste of building a full stack solution (from starting with data extraction, cleaning, and onboarding onto a data lake - to data mining, dashboarding, and making visualizations) as is done in many of Itility’s projects... To be involved in all these stages of a project has been a very cool and valuable experience. Overall, this really expands your perspective as data scientist, and helps you see the bigger picture of the work you’re doing.

YPP blog image
Some young professionals and other Itilians during a break-out session of our Winter Samen 

The use-case defines the contents of the program

Our main focus was to improve the knowledge app algorithm and add extra data sources (such as social media profiles). Although it was challenging, for me the fun usually starts when complexity kicks in. Because this was a real project, the decision of which tooling to use and technical skills to obtain was driven by project requirements. A good example of this was the improvement of our Python-skills: a tool that you don’t learn by sitting in a classroom for 8 hours. You improve your Python skills by actually using Python to build your own project in a team, solve challenges together and share knowledge. As mentioned before, I really value this learn-by-doing approach. You should be able to walk the walk, not just talk the talk, and apply skills on real-world use cases.

That is why, in addition to the technical skills, we strongly focused on our soft skills. As the workplace is completely different from the classroom, I found the focus on soft skills to be very valuable. As a consultant I learned how to present myself, to communicate clearly with customers and colleagues, to match my message to my audience, to give and receive feedback, and to be confident and position myself in an agile team.

Not changed, but grown

Our dedicated trainer guided us during the 4-month program, preparing us for our first customer assignment. Aside from the training sessions, I also learned a lot from our Scrum way of working. In our Daily standups we were provided with immediate feedback on our progress both as an individual and a team, constantly steered  into making small steps in the right direction. The power of the YPP is this attention to detail, stimulating you to grow into a better version of yourself.

Within 4 months we delivered a result that made our internal customer happy. The program prepared me and gave me the confidence I needed to join my current team at my first customer assignment; a great start to my data science career.

Our data science YPP has multiple starting dates throughout the year. Enthusiastic to join? Check out our careers site or contact us to see if there is a spot left in the next class. 

Daniel Lybeshari-rond (1)

Daniel Lybeshari, YPP software engineer: “During the program, where your ambitions and career plans are taken into consideration in shaping your path within the YPP, you get full attention as an individual. The program allowed me to step out of my comfort zone with confidence, which turned out to be a successful approach. I had the opportunity to face real-life challenges and resolve them within a diverse team."

An impression of the Young Professional Program Data Science 

Itility’s Young Professional Program (YPP) bridges the gap between the classroom and the workplace. It is designed for talented people who want to become data, software, or cloud experts, and have a maximum of 2 years working experience. The program speeds you through a variety of assignments and teaches you both soft skills and technical knowledge. In this article, we asked one of the former YPP-ers Annekoos Schaap to share her experience.

How I ended up at Itility? I started with a study in Electrical Engineering, and in my master focused on statistics, signal processing, and machine learning. I was really fascinated with the field of data science and decided to apply for the Young Professional Program at Itility. Although my studies were not purely data science, I made the cut together with three other young professionals. In my opinion, this is a testament to Itility’s philosophy: focusing on the individual’s mindset and motivation, not only on the skills listed on paper.

Jarrit Mannie-rond (1)

Jarrit Mannie, YPP data scientist: “I learned more in three months of YPP than I did in two years of university. Not only have I gained insights about my career but also about life in general. The structured way of working of the program can also be applied to your personal life. This helped me grow and become a better person.”

Itility is about culture, content, and changes

I chose Itility over other programs and traineeships for several reasons. The company culture and challenging projects appealed to me, as did the way of working within the YPP. Where I often saw alternative programs emphasizing their technical training, Itility focuses on contributing to real use cases. Learning technical skills, such as programming languages, sounds interesting at first and nicely fills out your CV, but eventually it’s all about applying these skills in your projects: it’s about working from the technical foundations that a company has, as well as being able to quickly acquire the required skills and knowledge to become flexible. When I had my job interview at Itility and learned about the first case I would be working on, it immediately felt right because of the challenge, the real-world dataset, and the value we would create for colleagues.

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