‘Once I started to see what was possible with data science, there was no going back’

Chanuki Seresinhe is head of data science at Zoopla and Hometrack. This is her career story so far – from researching beautiful places to helping web users buy, sell and rent property spaces.

Real estate
Stakeholder engagement

Brian Tarran


June 20, 2023

Hi, Chanuki. Thank you for sharing your career story with Real World Data Science. Please tell us a little about yourself and your role in data science.
I am Chanuki Seresinhe, head of data science at Zoopla and Hometrack. My commercial career in data science began in 2018 at Channel 4. Since then, I have worked at a few different companies – from startups to scale-ups – before ending up here at Zoopla in 2022.

I am also the founder of beautifulplaces.ai, which is a continuation of my University of Warwick and Alan Turing Institute PhD work where I provided the first large-scale evidence that beautiful places are connected to our wellbeing.

What does your job involve?
My role at Zoopla involves managing data scientists both for Zoopla and Hometrack. At Zoopla, we use data science to create an engaging experience for users who want to buy, sell and rent properties. At Hometrack, the data scientists mainly work on an automated valuation model that provides property valuations to most of the major mortgage lenders in the UK.

As a leader in data science, my role primarily involves helping stakeholders across the business to best leverage data science to reach our business goals, as well as ensuring my data science team has all the support and mentoring they need to design, develop and maintain high performing data science algorithms.

What does “data science” mean to you?
That is a really good question! One thing I have noticed is that people who aren’t familiar with the field often confuse data science and data analytics. There are indeed many similarities – both require quite a bit of knowledge to be able to leverage the correct insights from both structured and unstructured data (data hidden within images, for example). However, data science is essential when you need to make inferences. For instance, you are not only analysing data to see what certain consumers may prefer, but you are also predicting what similar consumers might prefer. Thus, having a strong grounding in statistics is really important for anyone working in this field.

Portrait photo of  Chanuki Seresinhe

Chanuki Seresinhe

In data science, getting the model right is not enough, and working with people across the business to make sure the model can be integrated into the business processes is essential.

What do you think is your most important skill as a data scientist?
Aside from a good grounding in the technical aspects of data science (which is possible for really anyone to pick up from the many good courses that are available), the most important skill is how you can leverage data science to create products that actually create value for the business. I find this to be the most challenging journey that junior data scientists find themselves having to navigate. They are really excited about the technology, and get carried away with wanting to perfect their algorithms. But when you are building commercial products, what is really important is to constantly engage with stakeholders to make sure you are building something that actually has a tangible business benefit. Early release of a model for user testing is also essential, as models only really get better once you have real user input.

How did you get into data science?
It was somewhat by accident. I previously had a long career in digital and decided to take a career break to return to university and study economics. When I was working on my Master’s degree in behavioral economic science at the University of Warwick, I saw an ad for a PhD to “use online data to understand human behaviour” and I thought this was perfect, as it combined my prior knowledge with a new area I was increasingly becoming drawn to. I quickly taught myself how to program in Python and convinced my supervisors to take me on, and from there on, I came to love data science!

What, or who, first inspired you to become a data scientist?
It was more about realising what you could do with data science. In my PhD, I was quantifying the connection between beautiful places and our wellbeing. While this has long been an intuitive connection, we were not able to test this on a large scale due to lack of data. Being able to use data science to start predicting the beauty of outdoor images was fascinating as it opened up a whole new method for potential research combining beauty with various wellbeing ratings. Once I started to see what was possible with data science, there was no going back.

What were the hurdles or challenges that you needed to overcome on your route into the profession?
For me personally it was the challenge of moving sideways into a leadership role after my PhD and not having to start all the way from the bottom. I would have loved to continue in academia and expand my research even further, but starting from the bottom earning a tiny salary after I had taken quite a large career break to do my PhD wasn’t an option for me. So I decided to go back into the commercial world and look for a senior role from the get go and luckily Channel 4 agreed to give me my first commercial stab at data science.

What are the challenges that you face now, as a working data scientist?
Trying to keep up with everything that is constantly changing in the world of data science. I love the rapid change but it can also be quite time consuming to make sure you are on top of it and giving the right advice to people.

What was your first job in data science, and how does it compare to your current role?
My first job was working as a senior data scientist at Channel 4. As a senior data scientist, even though you have additional goals to help run the team and be a mentor to more junior data scientists, you still get a great deal of time to do coding and develop your own projects. When you move more into a management role, the time you have to develop data science models diminishes. People also expect you to give in-depth guidance when you haven’t actually had much time to deep dive into a project. So, I am often trying hard to make sure I am on top of what is going on even when I have limited time, and really focus on building a strong team that can support each other and collaborate often to create better data science products. Learning to delegate is key!

What was the most important thing you learned in your first year on the job?
How hard it is to actually get organisational buy-in to use data science at scale. It is really easy to get approval to build a proof of concept (POC). However, if you do not use the time when developing your POC to also make sure to get the right stakeholder on board, your project is dead before it even starts. So, in data science, getting the model right is not enough, and working with people across the business to make sure the model can be integrated into the business processes is essential.

What have been your career highlights so far?
It has been great being able to give talks about my research, and data science in general, all around the world. I have actually come to love public speaking, and I hope that as I continue to be recognised for my expertise, I can encourage and aid potential data scientists with their careers – especially minority women, as I think that diversity in the field is very important. This is a role that is fit for people from all kinds of backgrounds and I hope I am exemplifying this.

Have there been any mistakes or regrets along the way?
In smaller companies, it can easily happen that the founders don’t fully understand data science and often use data science as a buzzword to get investors on board. Whenever you take on a new job, and data science is just getting established, make sure the founders or leaders are actually fully onboard with integrating data science into the product and understand what this means. See if they know how tricky data science can be when first integrating into a product and are actually willing to overcome the challenges with you to eventually reap the huge benefits data science can bring.

How do you think your role will evolve over the rest of your career?
I see my role evolving into being more strategic and less about the data science day-to-day modelling. It is more about being able to advise companies on how to make use of data science as a strategy and helping them figure out where in the product or process to inject it to get the most out of it for the business as a whole.

If you were starting out in data science now, what would you put at the top of your reading or study list?
Practise how you would apply using data science for a real life problem. Seek a placement, as this will pay dividends in being able to speed up your learning.

If you don’t understand the statistics involved in data science, make sure to upskill in that area before starting your first role. A lot of junior data scientists focus on learning how to code or get carried away with the modelling without first learning the importance of preparing the data in the correct way so that your predictions can work well in a real life setting.

What personal or professional advice would you give for anyone wanting to be a data scientist now?
Try to find ways to stand out from the average data scientist. When we open up applications for data science jobs, I get hundreds of applications for each one. I am looking for people who can not only do data science but who also have other stand-out qualities that they can bring to the business. This can be something along the lines of effective stakeholder engagement to deep expertise in a certain domain or technology.

What new ideas or developments in the field of data science are you personally most excited about or intrigued by?
I am really interested to see where generative AI will take us – particularly about how it can help us improve the speed of our own performance. It feels like generative AI can be a technology that can help everyone – even the everyday person – as it can help speed up so many processes, from coding to writing to ideating. While the technology is still in its early days, it is progressing rapidly and I am very curious to see where this will lead in the next year!

What do you think will be the main challenges facing data science as a field in the next few years?
Generative AI’s latest breakthroughs have made AI capabilities accessible to the broader public, but it has also stoked fears around the use of AI. The headline-grabbing narratives around AI and existential threat is distracting from other conversations that are really important. There are some very real issues we do need to solve – from biases in AI to the impact generative AI can have on wages and workforce – but this needs to be approached in a constructive and thoughtful way.

We need to find a way to engage with the public in a more meaningful way – rather than scaremongering – to have public debates on issues that actually matter.

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Copyright and licence
© 2023 Royal Statistical Society

This article is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0) International licence. Photo of Chanuki Seresinhe is not covered by this licence.

How to cite
Tarran, Brian. 2023. “‘Once I started to see what was possible with data science, there was no going back.’” Real World Data Science, June 20, 2023. URL