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!

About Antonio

Antonio is a senior data scientist at Digital Science, helping clients leverage machine learning to extract useful information from large-scale academic literature data. Prior to that, he was a senior data scientist the Wellcome Trust, responsible for developing tools to inform funding decisions and portfolio analysis. He holds a PhD in applied mathematics and has over 7 years of experience working in signal processing, data science and machine learning with organisations across the globe, such as AT&T Labs, Télécom Paristech, and Imperial College London. He is a former chapter leader for Datakind UK, a volunteer-led organisation using data science in the service of humanity, and in 2022 he was selected as an emerging leader in philanthropy by the Technology Association of Grantmakers.

How did you start out in your tech career?

I come from a very academic background. I did my undergrad and PhD in applied mathematics, and, even though the field has “applied” in the name, most of my work consisted of pen, paper, equations, and occasional help from symbolic computation software. In 2017, influenced by a friend, I applied and got accepted to a programme called Science to Data Science (S2DS), devoted to helping analytical phds transition into data science. I have been working as a data scientist ever since.

What are the signs of success in your field?

It might sound cliché, but success is a very personal concept, and can be achieved through various pathways. For instance, you might think that a successful data scientist is one whose work is recognised and who is invited to speak at many events. Equally, you might think that developing a piece of technology that influenced a real-world policy change or an impactful decision is a sign of success. Perhaps success for you means having a stable career, with great work-life balance, or perhaps you want to progress to manage a team of professionals. Data science offers all these pathways to success, as well as many others.

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

The most rewarding part of data science is being able to deliver insights from data that help people make better decisions, and to develop products that enable people to perform tasks otherwise impossible.

On the other hand, the over-hyped assumptions about what can be done, partly caused by some data science professionals, partially by broader social media means that a lot of our work is managing expectations and debunking myths. That makes us look like wet blankets. A colleague of mine once told me: “It looks like you don’t like machine learning – you are always trying to encourage us not to use it!”. And I stand by it – if a simpler, un-sexy, solution exists, it should be employed first!

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

Never stop learning. The field of technology is extremely fast-pacing and constantly changing. The best professionals are the ones who can adapt.

In addition, be very honest with yourself and your definition of success. Do you want to make as much money as possible? Or to have flexible work? Or work for a good cause?, etc.. Find what drives you the most, and work towards that end.

How do you switch off?

I switch off by quite literally switching off. Outside work, I try to turn off my computer and other devices and dedicate to non-screen-related hobbies. I love to read, run, play the piano, and to try out some of the quirky experiences that London has to offer.

What advice would you give your younger self?

I wouldn’t probably want to give advice to my younger self. I wouldn’t like to bias my decisions as a young man with the pre-conceptions that I have now. I think it is important to try things and make mistakes and learn for yourself. If I had to say something, I’d just reassure him: don’t be afraid to change career or jobs.

What is next for you?

I just joined Digital Science and am looking forward to seeing where the new move will take me. Digital Science is an organisation that produces a lot of the academic software I used as a researcher and being part of it is very exciting! Extra-curricularly, I just closed a cycle of 5 years as a member of the Scoping Committee of Datakind UK and just finalised a programme on Emerging Leaders sponsored by the Technology Association of Grantmakers, which will hopefully free me to pursue some personal projects that have been in my bucket list for a long time (those data science notes are not going to write themselves!).

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

I would probably be working full time towards social impact and reducing social inequality. I still think that that’s where I will go in the long run.

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

1. Data science will become more and more a well-established umbrella term (such as Computer Science). We have already seen a number of organisations offering masters, and even undergraduate programmes in data science. This trend will probably continue, and data science might overtake other traditional courses.

2. Software development will become second nature to the next generation. More and more kids will learn principles of computer science earlier in the journey. To a certain extent, knowing software development will probably be the next version of having “Microsoft Office skills”.

3. In the machine learning world, large pre-trained models that perform well on a number of tasks will become the norm, will decrease in complexity, price, inference time, and will become increasingly popular. The work of the data science practitioner will be to identify the best one for their needs, and translate them to business.

4. Data science will become centred around data infrastructure, from acquiring data to deploying data-science solutions. The data-centric AI movement is an initiative that explores this idea, although it seems to have lost steam over the last year. We have already seen over the last year a proliferation of roles around “machine learning operations” and “data science engineering”. This trend is likely to continue over the next 5 years.

5. I hope that the number of organisations using data and data science for social impact continues to increase over time, alongside philantrophists sharing and using data to mitigate gaps in the grantmaking space.

Watch Antonio’s session at the Data Science Festival here.

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

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