An internship at Agilytic: Clément’s experience

Joining a team of skillful, supportive data scientists to challenge you and help you grow is crucial at the beginning of your career (well, at any stage really!).

With a data science internship, you have opportunities to contribute to real value-adding projects such as project deliverables or internal asset development. You have exposure to the day-to-day life of a growing data science consultancy practice. And, you have time to learn fundamental skills in data science and opportunities to apply them to actual cases.

Hear Clément Soens's perspective as he describes a challenging, fun, and supportive work environment at Agilytic. Clément has been working on real consulting projects since his internship and is now employed at Agilytic.

1. Tell us about you and what events in your life/career brought you to apply to the data science internship?

I studied at the Solvay Brussels Schools of Economics and Management, and I'm part of the QTEM master network. My passion for data began with the several data-driven projects I worked on during exchanges at HEC Montreal and Luiss Roma. Then, I tried to find an opportunity not so much in management, finance, and economics, but more technical, in analytics and data. I took some MOOCs and online courses in programming, especially Python and Matlab.

At Solvay, Professor Martine George, an advisor at Agilytic, told me about the company and suggested that I apply as an intern. I applied, had a quick call with Julien Theys, and started soon after on a 4-month internship.

2. Tell us about the project or projects you worked on during your data science internship.

I appreciated that I had a choice between two main projects. I chose fraud detection in documents and image processing - of which I had no experience, and that's why I chose it. I wanted to do something I'd never done before. Agilytic trusted me with that - thanks, Chris! Before I started, I did research to know what was going on and reviewed what was done before previously with large banking clients in Belgium. I didn't want to reproduce someone else's work. I really wanted to do something different. I started with a lot of academic research about image processing, signal transform and mathematical compression. I then choose to work with discrete cosine transforms for pixel edges noise detection.

At the start of the project, there was no direct link with a client. It was more about Research and Development. I did data analytics and data prediction using Matlab and Python. As part of this project, I worked a lot with OCR modules and finally developed a few useful functions - and those new features interested one of Agilytic's clients. Now, I'm working on this project as a Data Scientist with a client specialized in debt collection. It's really cool to see something with a tangible application a client will benefit from. I've also been working on getting certified in AWS, which will help me use the cloud data infrastructure for future projects.

3. Why do you believe in Agilytic's services?

I like that while there's clearly a business engineering element, it's true that we do data science. But there is a business focus, with real business value. We make sure a project doesn't get too complicated in engineering and technology if there's a more efficient and cheaper way. We do not stick to one formula, but we try to find the best solution from client to client.

4. What development opportunities/skills were you exposed to during your data science internship?

As an intern, I developed two types of skills. Firstly, technical skills. I worked with Matlab and Python before, but I became very comfortable with them by the end of my internship. I had the chance to deep-dive into new modules and libraries, either for the needs of the projects or that came recommended by colleagues. There is a natural learning ecosystem of data analytics and data science at Agilytic that helped me gain new skills in this. Secondly, my business and soft skills improved with insights after directly working with our Managing Partners, Chris and Julien. This honed my understanding of managing expectations, giving presentations, and following through on deliverables with a client. We worked a lot in the Agile method for continuous iterations and improvement on projects. There's a culture of learning by doing here.

5. Do you feel like certain prerequisites or backgrounds are necessary to succeed?

When I chose my internship subject, I felt it was pretty technical, and I wasn't sure I had the right background. I trained as a business engineer, and my internship project required more engineer skills. I asked Chris, my supervisor, to do only training in the first week (Kaggle, online training) to gain skills and feel more confident about the task.

So, I would say there is no perfect profile. If you look at our colleagues, there is no classic profile - business, engineering, physics, law - many different backgrounds. Just be ready to learn!

6. What are the three attributes that you think make someone a great fit at Agilytic?

At a minimum, I think you need to be into programming. It would help if you were open-minded as you'll work with many people from diverse backgrounds, different technologies, and programming languages. Also important, don't be afraid to show your fun side - we're looking for our next billiard champion!

7. How did your data science internship prepare you for the next career step as Data Scientist?

Of course, it prepared me very well in that I am a Data Scientist now, working for the company. I am working towards a Ph.D. thesis in econometrics. Working here is nice as there is a real connection between Agilytic and my Ph.D. thesis in an academic setting. The skills I gain in OCR I will also apply in my thesis.

8. What stands out to you about the culture at Agilytic?

I would describe the culture at Agilytic as flexible and trusting. During my internship, we would have weekly check-ins - there was no daily monitoring or 'hand-holding'. I really appreciate the level of autonomy we have, allowing us to learn by doing and to make the next best decision. However, whenever we're stuck and have a question, there is always a colleague available to help.

Also, we had a very fun offsite in Lille. It was super enjoyable and a great way to get to know one another.

9. What gets you most excited about coming to work? What do you like the most about what you do?

I really enjoy client meetings where we show our work. They make you feel proud of your work, and it's great to discuss the project and collaborate this way. I like every day that there is always something a little bit new to work on. I've never had twice the same day. And I'm surrounded by nice colleagues!

Think you could be a good fit?

We are looking for curious and motivated interns to join our team. During your data science internship, you will help us to deliver faster and better results for our clients while gaining skills in new languages, methods, and technologies.

Discover more and apply on our careers page!