After obtaining a master's degree in Business Engineering, Arnaud worked for three years as Business Consultant in the ICT sector. During this period, he became familiar with Agile methodologies and project management.

Passionate about Machine Learning, Analytics, and Data Engineering, he pursued his learning journey by getting a second master's degree in Data Science at UCLouvain, graduating in 2021.

He’s since been at Agilytic as a Data Scientist. Balancing both his business and technical skills, Arnaud thrives in assisting companies in solving their distinct problems through the smarter use of data.

Hear how he found his way to a role in Data Science and what he's learned and worked on so far!

Tell me in a few sentences about you and what you're currently working on.

I've been at Agilytic working as a Data Scientist for almost 4 years. Before it was not a straight path, I graduated a few years back in business engineering, which introduced the subject of data science in a few classes but didn't go too deep into it. After that, I spent a few years as a data consultant, analyzing data on the business side and not so much developing solutions. I became interested in learning how to develop solutions. I always liked the intersection of mathematics, statistics, and coding. To pursue all these subjects combined, I started my new master's program in data science and loved it and never looked back. So it's the small steps that landed me in this position.

What are you currently working on?

I’m currently working on several computer-vision pipelines for scanned documents. The goal is to extract information from scanned documents. We still rely on specialized OCR and layout-analysis models to detect text blocks, tables, and visual patterns. On top of that, multimodal LLMs have, in the last two years, dramatically improved our ability to interpret and clean up the OCR output. They fill in missing context, and convert the results into structured JSON or database records that can easily be parsed by a company’s chatbots or internal search engines.

It's cool because every time I have a new demand or task to do, I consider the ways we can approach it. Most of the time, it's very precise in a project related to specific documents that haven't been processed like this before, and we need good accuracy for good results. It's quite a learn-as-you-go process where you research how to do something and apply algorithms you know already while needing to learn new ones, and you have to make changes along the way. It's like trying out different ingredients to make a recipe.

What does your average day look like?

I might have a weekly meeting with the client or Agilytic, discussing the project, our progress and what we're doing next. Then, I'll work on coding and trying to find the solution and do research. I might look at what I can do to maintain existing solutions. From one day to the next, I'm working on developing a particular solution. On another day, I might have several meetings, deciding the next steps for what we'll work on. It's a bit of a mix.

What's your favorite project you worked on or are currently working on? Tell us about it.

My favorite project is predictive scoring from a marketing context. It was pretty challenging to try to predict how people behave. I learned much about the contextual variables you can and can't always consider and how to deal with those variables. It's interesting to dig deeper, learning about the business needs and how clients sell their products.

How did you find your way to your current role at Agilytic?

I was looking for a new opportunity in data science as I was beginning my new master's. I wanted to see what companies were in the field. I discovered Agilytic through social media, I believe, from a post about a project we'd worked on in the past. Already I could tell it would be a nice fit, and I was already working for a smaller company, working with large clients. I didn't want to work in a large company where it's tough to know who's doing what. So I tried to find a similar environment in data science, and Agilytic matched this quite nicely.

Could you share a few details about the favorite part of your role?

I am constantly learning. I'm the kind of person where if I'm doing over and over the same thing, I'm getting bored. Outside of work, for instance, if I'm cooking, I like to try some complicated recipes I haven't tried before. If I've cooked it before, I'm not interested in doing it again. So I like that I can do the data science equivalent in my current role.

What is something about Agilytic that you appreciate? Maybe that you were surprised by?

The project ownership and autonomy are really refreshing. It’s definitely harrowing at first but once you make it through the first project or two, I can safely say that I appreciate the implied confidence and trust.

What is your "why" concerning data science?

I find data science quite motivating, as it brings value to people and can help them, often with the data they already have. Clients are quite happy with what they get in return for the project's output. I've heard, "If I had this five years ago, it would have changed so much," and they realize they can go so much further when they understand their data. Also, I have projects where I practice the teaching aspect of consulting. I enjoy that very much. We try to emphasize the hand-off with clients, so data science is something they can continue on their own.

How does your experience in the ICT sector influence your work now?

I learned the usual steps of projects and what is vital in defining the needs and scope before going further with a solution. Moreover, I learned how to manage smaller tasks like solution testing to big-scale projects and handling client relations and changing demands.

What have you learned at your job, and how have you grown? Skills you've been excited to learn?

I've learned a lot of hard skills, such as working with tools. Before arriving, I never used cloud solutions like Azure or AWS. These are very handy tools and are growing more popular. We try to keep things cost-effective as they can be expensive. The most prominent learning point is my exposure to cloud technology.

How would you describe our team?

It's great to work with people who are passionate about their job and what they do. I like the trust and relations we have at Agilytic, enabling a hybrid work environment. Everyone is very nice too!

What advice would you give to someone considering a transition to data science?

I chose the master's route, but you can do it another way. So many helpful online resources can help you develop a good understanding of business intelligence in general, covering all the basics to step into data science. Then try out for interviews, research the questions interviewers ask, and practice becoming more confident in your answers. It's feasible if you're motivated!

What do you like to do in your free time?

Recently, I've been playing a lot of squash, trying to motivate friends to play padel tennis too, and would like to get back into a rock climbing routine. I also like to read sci-fi and fantasy books, of which I can recommend some recent titles I read like 'Le nom du vent.’ It's excellent, but you have to be patient to wait for the next volume as it comes out every seven or eight years.

Sounds like a nice fit?

We are looking for curious and motivated data scientists to help clients make smarter decisions with data.

Agilytic-ers are driven to provide our clients with actionable insights that translate into tangible improvements for their business.

Are you looking to gain skills in new languages, methods, and technologies? Do you have a passion for business and an entrepreneurial sense?

→ Discover more and apply on our careers page!