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 Gabriel

I joined Ocado Technology in 2020 as Chief Data Officer, bringing over 10 years’ experience in leading data science teams and helping organisations realise the value of their data. At Ocado Technology, my role is to help the organisation take advantage of data and machine learning so that we can best serve our retail partners and their customers.

I am also a guest lecturer at London Business School and an Honorary Senior Research Associate at UCL. I have advised start-ups and VCs on data and machine learning strategies. Before joining Ocado, I was previously Head of Data Science and Architecture at the BBC, Data Director at, and Head of Data Science at Tesco.

I hold an MA in Mathematics from Cambridge and an MBA from London Business School.

How did you start out in your tech career?

I started my data career in 2012 by joining Tesco with the task of building an advanced analytics team that could develop algorithms to optimise the general merchandise supply chain. Over time, my role expanded to cover domains ranging from search and recommendations, to last mile and price optimisation.

After 4 years at Tesco I joined notonthehighstreet. I was brought in as Data Director to help bring data awareness and expertise to a very creative organisation.

I then joined the BBC as Head of Data Science and Architecture. The challenge was to build on organisational changes and bring together different data sources and make the BBC more data and ML-savvy.

And then, in 2020, I joined Ocado Technology which is where I am now working as Chief Data Officer. My role is to make data and ML a compelling competitive advantage for the business. I support around 130 data people covering data engineering, data governance, data analytics, data science and simulation that are mostly embedded in our development teams.

What are the signs of success in your field?

I think it depends on the maturity of the organisation. If you first introduce data somewhere, the initial success is that everyone talks about data and starts to measure key metrics. As your organisation matures, success means that you don’t need to talk about data anymore because it becomes embedded in the way you make decisions.

For my leadership team, one of the key ‘metrics’ I care about is how their engineering and product partners feel about the data organisation. Do they think that we create value? Do they want us in the important conversations? Do they reach out to us with difficult questions?
For the teams building ML algorithms, they have specific metrics that they try to optimise depending on the application and where we are in the development lifecycle.

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

The best thing about my job is hands down the diversity of data problems at Ocado Technology. We use ML for anything from recommendation engines to demand forecasting and order optimisation, from spotting bots on (fish-eyed) CCTV camera footage to guiding robotic arms to pick groceries. It would be hard to get bored in that environment. Ocado Technology also has a very open culture and so it’s easy to approach anyone about anything they are working on. I really like being able to take this wide perspective and then being able to go into the details of particular areas if I want to.

The most challenging part of my role is probably coordinating data efforts across a large organisation. Today we have nearly 3,000 technologists working across 7 countries so alignment is not always easy. One of the USPs of our platform is that we can optimise across the integrated end to end grocery ecommerce, fulfilment and logistics process. This means collaboration across different platform areas is crucial. Luckily, the company culture, combined with the modern tooling we use, helps our global teams work effectively.

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

I think success looks very different for different people. Some people want to run a big team, others prefer to be a real expert in their chosen area. It therefore helps if you understand what success means to you. After that, I would look for mentors and people to learn from, in particular for the more leadership and human sides of the job.

Another area that many successful data people have in common is a deep understanding of the business challenges. What do your customers care about? How does the P&L of the company work? That helps explain the benefits of your work in a way that can be understood by non data people. And successfully landing a data solution in an organisation is often harder than doing the maths.

How do you switch off?

Early on during the pandemic I picked up the two very hipster hobbies of running and making sourdough bread – although I feel that I sometimes have some of my best ideas when I go for a run, so not sure I’m doing such a great job at switching off!

We also have a young child and spending time with him helps a lot with putting stuff into perspective.

What advice would you give your younger self?

That’s a difficult one. Maybe two things:

1. To study computer science rather than maths, or at least both together. But to be fair, there is now so much online learning available for free that the degree you study is becoming less and less important.

2. When I started my data career, there was a lot of organisational change in my first year. And I got quite stressed by some of it. In hindsight it provided me with some really important learnings that I value to this date. So I would probably try and convince myself to not get too stressed by things like this.

What is next for you?

I have only been in my current role for just over two years now. And a big part of my role is to try and drive changes of how we use data and think about ML. In my experience these efforts tend to take a few years and I am not done yet. So I haven’t spent much time thinking about what might come next.

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

I really like the following quote from Bill Gates on this topic: “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten”

That being said:

1. The world will continue to move faster and faster. Customers will continue to want their experiences to be more relevant and quicker and cheaper.

2. AI first is the new mobile first. And it’s 10x harder to make that switch. We will see more of a separation in performance between companies that are data savvy and those that have not been able to mature their data capability.

3. Just like the best chess player isn’t a human or a machine but a human and machine team, the future of coding will be much more co-development between algorithms and computers. The best technologists will be those who can leverage these new technologies and bring their own experience and viewpoints to solve real problems in new ways.

4. With capital becoming more expensive, companies will have to go back to the fundamentals of managing cash. This means that data and technology teams will have to be much better at understanding and explaining value. Saving costs now will become more important than growing future revenue.

5. The regulatory framework will continue to evolve with AI in particular getting more attention from lawmakers across the world. Regulation will also diverge more across different geographies creating a challenge for technology teams on how to best serve customers across borders.

Watch Gabriel’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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