Alessandro Arlandini

Describe yourself with three to five words?

Curious, analytical, passionate, open-minded, adventurous.

What's the short version of your CV?‍

I am a pure mathematician who switched to data science. After completing my studies in Mathematics at the University of Padova, I started a PhD at the University of Warwick in the UK. In those 4 years I did research in Number Theory, a field of abstract Mathematics. I finished my PhD in 2020, and while I didn’t want to continue on the academic career path, I still wanted to be part of a scientific-minded environment that catered towards innovation. The sector that attracted me the most is data science and machine learning. I worked for one year and a half as a data scientist at an early stage startup in Oslo, tackling problems related to domestic energy consumption and electrical mobility. I joined Arundo as a data scientist in April 2022, attracted by the chance to work on problems of predictive maintenance, condition monitoring, process optimisations and others.

What excites you with your line of work?

‍I like the scientific challenges that arise when you apply data science to real-world cases. When I’m faced with a business problem, I carry out an investigation. I look for patterns and insights inside the data, form hypotheses, test them against the evidence, and draw conclusions. At the end of this process I have to come up with actionable solutions, and it is very satisfying to see the impact they have. In addition, the varying nature of these challenges makes it always exciting.

How and why did you decide on Arundo?‍

What captured my interest is the kind of problems that Arundo strives to solve, and the technical depth of its approach. The idea of improving industrial operations with cutting-edge analytics means that I get to work on innovative tech which has an impact on a very large scale; that’s a great motivator for me. I also knew Arundo for having a healthy and tech-friendly work culture, which was confirmed in the first approaches.

What are you currently working on?

Currently, the project which takes up most of my time is about helping the maritime shipping industry achieve better operations. On one hand, this means reaching a higher productivity by identifying effective performance drivers and then optimising for them. On the other hand, we are applying techniques of predictive maintenance and anomaly detection to make heavy equipment last longer and operate more efficiently.

Describe your “typical” contribution to an Arundo project?‍

What I typically do in an Arundo project is leverage data-driven products to solve the customer’s problems or add value to its operations. In the early phases of a project, I support the delivery team in working together with the customer. At this stage, the goal is to pinpoint single use cases that strike the right balance between value added for the customer and technical feasibility. My main contribution is in the subsequent phase, where I work with the data to extract actionable insights and products that benefit the use cases. In this phase I explore and analyse the data and build models, and I use different ideas and approaches depending on the end goal.

What would you choose if you could solve any problem within the heavy asset industry tomorrow with a solution delivered by Arundo?‍

I would choose sustainability, starting from the ability to diagnose the impact of single assets on the overall operational footprint, up to precisely modelling production scenarios.

What would you say is Arundo’s key advantage as an employer?‍

The key advantage of Arundo as an employer is the fact that it creates an environment where you can thrive. I feel incentivised to grow personally and professionally, that my opinions are taken into account, and that I am supported in performing at my best. I am also working alongside talented colleagues, and that creates many learning opportunities. Arundo is a company that puts you in the right context to succeed and takes care of you all-around.