Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!

A bit about Fabio

I am an impact-oriented and passionate Data Scientist. Active from 2017 after a PhD in Data Science. At the beginning of my career I worked in a Commercial and Marketing environment, focusing on Data-Driven Attribution, Propensity Modelling and Incrementality Analysis. Then, I moved to a role closer to User Behavioural analytics, working in a Product domain. Here I worked on different projects to model user behaviour and identifying exogenous/endogenous factors through supervised and unsupervised approaches. Lately, my focus has shifted towards Deep Learning and Computer Vision for Document Intelligence.

How did you start out in your tech career?

I have always been leaning towards science subjects, so I enrolled in Telecommunications and Telematic Engineering. I discovered Data Science thanks to my master thesis, when I had a chance to apply this discipline to Network Data. It was love at first sight and I decided to continue my studies with a PhD on Data Science. I was probably very lucky to stumble upon something so cool and interesting almost by chance.

What are the signs of success in your field?

Ideally, making an impact with the simplest possible solution. However, sometimes it does not depend on you. I can tell you what’s not a sign of success for sure: just pursuing complexity for the sake of fancy approaches.

Let’s say that being able to persuade some skeptical stakeholders to adopt a data-driven approach and starting to collect some easy wins is a great achievement. To accomplish this you need passion, knowledge of the subject, rigor, but also good story-telling skills and understanding of the business problem.

What is the best and worst thing about your job role?

Discovering new insights and people’s behaviours really makes my day, especially when it sheds light on new angles of product usage. And you might be the first to puzzle it out in your whole organization. That, together with the continuous exposure to new technologies and techniques, is the most satisfying part.

The not-so-great part is that you would usually depend on other teams to really make or measure the impact of your work, and I am obsessed with it. A new insight, a model, a simple experiment. Not being able to quantify the impact of your work can sometimes be frustrating.

What can you advise someone just starting out to be successful?

I would recommend not skipping the basics. They are really important and will probably become even more important in a world where it is really hard to compete against pre-trained black-boxes. At the same time, I would always try to refer any type of learning towards a few examples of business problems. I believe most of my top contributions so far were not related to the mere complexity of the approach, but to the ability to map a business model to a well-known data science solution.

How do you switch off?

I love playing video games and cooking, especially for my friends. That relaxes me a lot.

What advice would you give your younger self?

Never stop actively learning and reading. But do not underestimate the value of ‘passive’ learning: keep going to Meetups, follow data scientists on Twitter, have a random read on Medium…

Also, do as much as you can in the morning before work.

What is next for you?

I can’t imagine any other possible answer than to keep learning.

Currently, I am raising my game with Deep Learning and Computer Vision.

If you could do anything now, what would you do? Why?

As any Data Scientist, I have a few models and ideas that never made it to prod for different reasons. I would be very curious to set some of them live, with a proper evaluation framework in place. Iterating on a model in production is a unique learning experience, really far away from anything else.

What are your top 5 predictions in tech for the next 5 years?

1. It will be harder to compete with pre-trained models or specialized companies in some domains
2. …but Data Scientists will be needed more than ever to adapt, tune and evaluate such techniques.
3. I might expect even more jaw-dropping language models, but I would be very surprised if they completely solve the issue of incorrect (but plausible) answers. Issues you might incur currently with the best language AIs.
4. More and more non-analytics employees will start to have basic Data Analytics skills..
5. …but a lot of companies will still be in their first chapter of their data journey, with a lot of low hanging fruits. Good for us!

Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!

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