Papinder Dosanjh, Head of Data Science and Machine Learning, ASOS

What has been your path to power?

I started my career in a start-up specialising in geo-location solutions for media, retail and telecoms clients, before joining leaders in customer science, dunnhumby. During my time there, I helped high-profile FMCG companies use shopper data to build relevant category, trading and shopper marketing strategies, and as senior product manager, I was responsible for developing a personalisation capability for a major supermarket across its e-commerce, loyalty and CRM functions.

I then decided to move on to Accenture to build experience of different industry verticals. My role covered global digital transformation initiatives, including delivery of cloud-based big data platforms and digital messaging capabilities. Transformation was also a theme at my next job in business strategy at TUI, where my focus was on defining the global “one” analytics strategy for the executive board and championing the creation of an analytics centre of excellence.

Now at ASOS, I’m responsible for executing the business and technology transformation behind our data and AI strategy. We’re working to embed data science and machine learning across all areas of our customer experience, and also to support operational excellence.

Does data now have a seat at the table during strategic discussions? If not, what will it take to get it there?

Data is becoming increasingly important as our business scales. Data as fuel is one of our strategic business transformation initiatives which has executive ownership. We will be accelerating our investments to support future growth ambitions and plan to scale our data operating model and evolve the data architecture over the coming year. To support this, we launched a new technology hub in Belfast focused on growing our data engineering capabilities.

What are your key areas of focus for data and analytics in 2022?

2021 focused on defining our data strategy – 2022 will be focused on implementing the data strategy and mobilising the transformation initiative. Key focus areas will include embedding a new operating model, driving data literacy programs and executing the prioritised use-cases. For data science, we’ll continue to mature our MLOps practices so we can increase the pace of iteration and improve the productivity of how we build, deploy and serve our models. This will help to scale the number of AI use-cases and maintain high-quality ML systems.

What key skills or attributes do you consider have contributed to your success in this role?

Essentially, I focus on three three things: creating a culture of scientific excellence; hiring talented and diverse engineers and scientists; and working on the right problems that delight customers and deliver operational efficiencies.