Headline Partner

Mahana Mansfield, VP Science, Deliveroo

Describe your career to date

After finishing my PhD in maths and retiring as an international judo athlete, I had no idea what I wanted to do with my life. I found myself at Ocado, specialising in consumer facing algorithms, including personalisation and recommendation engines, and getting an excellent grounding in what would later be called data science.

After Ocado, I spent some time at the Guardian, where I was head of data science, helping them to understand more about their most loyal readers and what type of content they were interested in.

I moved to Deliveroo in 2017, at an exciting time for the company, and was intrigued about how much there was to learn in the online food delivery space. My time here has been a real highlight of my career to date, as I’ve been able to help Deliveroo make exponential improvements to its product offering for consumers, riders, and partners.

Today, I look after our whole data and science organisation, which underpins every corner of our marketplace. We use data to help Deliveroo make difficult decisions, balancing the needs and interests of the three sides of our marketplace (riders, partners and consumers) to do the best possible thing for all involved. In addition to making sure my organisation is running effectively, my role allows me to play an active role in solving some of our most challenging business problems.

What stage has your organisation reached on its data maturity journey?

As a young, data-driven company, we are lucky to have had access to advanced technologies and knowledge of best practices right from the start. All of our systems are natively built using the latest technology such as Amazon Web Services, and consider data from the get-go. Of course, not everything we try will be perfect the first time around, particularly when we are testing out new features or developments, so we do need to make sure that our constantly evolving software is created in a way that protects the integrity of our data definitions.

Tell us about the data and analytics resources you are responsible for

I am responsible for the data and science organisation, which is currently just over 250 people. The teams work across four key disciplines: machine learning engineering, which includes operational research (building machines that make automated decisions); data science (optimising human decision making by improving our ability to model future outcomes); analytics (helping teams across Deliveroo to understand our business performance and make better decisions from the available data); and analytics engineering (making sure we have high quality, reliable and usable data). I report to our chief technology officer and sit on the company’s internal leadership council.

What challenges do you see for data in the year ahead that will have an impact on your organisation and on the industry as a whole?
In the current macroeconomic climate, there is greater pressure on organisations to invest in areas with guaranteed impact. Within data, we will need to focus on getting the basics right first, before trialling more novel or unproven tools and techniques. At Deliveroo, we believe that solid foundations are the best path for future success, and we should always start with the simplest plausible approach.

Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organisation or just the data function?

My vision for the data and science organisation is to enable the highest quality human and machine-led decision-making across the company. This means that everyone in the business can seamlessly access, analyse and use data to make the decisions required in their role, from which restaurant or grocery partners we should be working with to how many riders we should have on the roads at peak times in Paris. As a company, we are passionate about the power and potential of automation to optimise decision-making.

Have you been able to fix the data foundations of your organisation, particularly with regard to data quality?

As I mentioned previously, we’re really proud of our data foundations as a tech-first company. For us, it’s more about making sure we protect those data structures and their quality as we deploy new software at pace. This is reflected in our ways of working, for example by prioritising certain metrics and making sure they are appropriately supported.

Mahana Mansfield
has been included in:

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