Anna Vissens is Lead Data Scientist at The Guardian in London. For our interview series with women working on the intersection of AI and journalism, Anna spoke to us about a career that has gone from dreaming of space travel to working at the cutting edge of developing newsroom technologies.

JournalismAI:  What is the most interesting part of your day at work?

Anna:  Working out the best way of data science integration with the business is a pretty exciting challenge. All the data scientists here at The Guardian are dedicated, creative, keen to learn and share their knowledge. It is the daily interactions with the team that brings me great joy.

From a professional point of view, getting something done, watching your model running and producing results that make a difference to the business is the most rewarding part of our job.

What kind of data and AI projects is your team working on at the moment?

The Guardian’s data sets are rich and varied. Because we have different revenue streams, the range of problems we have to solve is very diverse, which makes our job challenging but fascinating. It is always a joint effort across different teams, including data analysts, data scientists, and data engineers.

Projects we take on board reflect our values at The Guardian. We care about inclusion and diversity and we are very keen to understand the best way to measure these in our content. Are there any gaps in our coverage? How diverse are the voices we use in our news reports? These are types of questions we are trying to answer with data. Embedding data in our decision making process is crucial. But it doesn’t mean we take a blind data-driven approach. We trust our editors and journalists when it comes to creating outstanding independent journalism. Data helps us zoom out, see a bigger picture, and make right choices.

When was the moment you decided that you were interested in data science?

I am a physicist by trade, but I spent many years working in journalism. I started as a freelancer and then moved from one role to another exploring every corner of the media industry, including as a multimedia producer and social media editor. I have truly enjoyed every single job I have done in my life, but I always had a secret dream to move back to science.

When I was working as a project and product manager at the BBC I was plunged into analytics. After a while, I realised that a different route back to science existed and I was standing at its gate. I spent all my weekends doing online coding courses, first pretending it was just a hobby. Evening data science classes and a fellowship in machine learning finally shaped my career aspirations. I was very lucky because I was surrounded by people who believed in me and encouraged and supported me all the way.

Who inspired you to pursue a career in science?

When I was a kid I actually wanted to be an astronaut! My older brother, Miguel, was born with cerebral palsy. He always wanted to study astrophysics. My parents bought him a telescope and his bedroom was full of sky maps and physics textbooks. I remember spending nights together with him looking at the sky and trying to identify different planets.

Unfortunately, his dream of becoming an astrophysicist remained unfulfilled. He studied foreign languages instead and became a translator and a freelance journalist. He was a great inspiration to me in many ways. And sometimes I think that his influence on my life meant that I somehow made his dream come true too.

As a successful woman in the industry, what advice would you give to the younger generation with regards to working with advanced and emerging technologies?

When I was studying physics at university, there were only a couple of girls to twenty or so boys. The world has changed dramatically since then and we are seeing more and more girls applying for natural sciences. This is great, but there is so much more we, as a society, need to do so we do not lose these talented people when they face career challenges.

If we are talking about data science, I can see this field becoming over-crowded. Machine learning technologies are changing at a tremendous pace. The only way to stand out is to constantly learn new things no matter how hard and daunting the learning experience may seem. Choose what you are really interested in and become the best at it.

Is there a specific reason you chose to work in the media field first, although you are a physicist by trade?

I am afraid my early career choices were not real choices, but a survival strategy. I started working in the insurance industry as it was very difficult to stay afloat working in academia. I made quick progress through the ranks and ended up leading an international reinsurance department. But when my son was born I had to start all over again and freelancing was a very attractive option offering flexible hours (too flexible, as it meant I worked quite often at night) and working from home. I still remember this period of my life with nostalgia. And it did not stop there. I was always very keen to learn new stuff. This is how I moved from one role to another. Some parts of this ride were bumpy, but I always tried to transform failures into motivation to move forward and change my life for the better.

What are your thoughts on the impact of these new technologies on society?

Although the buzz phrase ‘data science’ is relatively new, areas such as mining and wrangling data, modelling and getting insights, predicting future data points — all these have been going on for decades. What has changed is the volume and sources of data, computational capacities and cutting-edge methodologies, such as deep learning. These models indeed change our lives; they change the way we behave, make our choices and how we interact with each other.

We all produce tons of data every day, by using our digital devices, health tracking apps, streaming services etc. This data allows people to create models that can predict behaviour. These models become more and more ambiguous and as Yuval Noah Harari wrote in his recent article about the Covid-19 pandemic: “the same technology that identifies coughs could also identify laughs”.

While we are talking about regulating this field, preserving ethics and making sure that our models are transparent, explainable and fair, I would insist that the broader education of the public, especially when it comes to critical thinking, is crucial if we want to preserve our democracy.

The interview was conducted by Tharsa Sakthipakan, Polis intern and LSE’s MSc Student. It is part of a JournalismAI interview series with women working at the intersection of journalism and artificial intelligence.

If you want to follow the series and stay informed about JournalismAI activities, you can sign up for our monthly newsletter.

JournalismAI is a project of Polis, supported by the Google News Initiative.