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Reda Kechouri, Head of Data Science, Bumble

What has been your path to power?

While my education is rooted in computer science and engineering, the continuous concern throughout my career has been how best to use the data at hand to help organisations make the best decisions at the right time – at Transport for London, for instance, when building real-time traffic information systems for the London 2012 Olympics, or when designing software to help global retailers optimise their floor space and product allocation. 


In parallel, I became increasingly interested in organisational design, team empowerment and how best to drive an outstanding culture that focuses on impact. In that sense, my experience at e-commerce fashion retailer ASOS.com was truly transformational for me as I had the autonomy to build the AI platform from the ground up, following these guiding principles: autonomous multi-disciplinary teams, iterative approach to model building, operational excellence and focus on impact and business value.


Today at Bumble, I aim to create the best environment for our incredible talents in statistics, machine learning and engineering to fulfil our mission to make all relationships healthy and equitable using the amazing datasets available to us.

What impact has the pandemic had on the role of data in your company/organisation?

Covid-19 radically changed the way our users find meaningful relationships and proved to be instrumental in Bumble becoming even more data-driven. For instance, when the pandemic hit, we explored and leveraged external data sources (Oxford Stringency index, Google mobility reports) that would improve our in-house developed forecasting framework and anomaly detection processes to help us navigate these uncertain times.


The business obviously needed quick access to these insights to make swift decisions, while working from home became a norm. This exceptional context proved to be a catalyst for improving how data is disseminated across the organisation. We successfully delivered a bunch of self-serve dashboards in close collaboration with all business units. 



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

As a product- and mission-driven tech company, Bumble naturally puts data at the heart of its decision-making and strategy-shaping processes. For instance, we use data extensively in our annual planning process to formulate the strategic discussion points and drive product development. This data-driven approach necessitates sustained efforts in maintaining and evolving our strong data infrastructure and capabilities, as well as continuously driving a culture where every decision taken is based on tangible data and insights. This approach is happening at all levels of the organisation and decisions are made faster as data is widely distributed across the company.


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

In 2022, we will keep accelerating our data transformation, both from a technological and a cultural standpoint. Concretely, this means continuing the work on self-serve analytics coupled with a clear strategy and programme of work around data literacy to reach all audiences. We will also continue our shift towards our target cloud infrastructure to enable more flexibility for our data operations. Finally, we will be expanding our machine learning teams and capabilities with ambitious hiring plans and strong investment in core machine learning engineering toolsets and experimentation frameworks.


Tell us what leadership means to you in the context of your role as a senior data leader.

It might sound like a cliché, but it all starts with a clear vision. This includes understanding the strengths and weaknesses of the current data set-up, weighing the opportunities at hand to fulfil the business mission and objectives, and having the communication skills to listen to our people and convey a compelling data story both internally and externally. I tend to focus my efforts around these four pillars when managing my data organisation: talent, culture, mission and operating model.


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

Beyond any technical knowledge I may have, the key to my successes has clearly been my people-first approach to problem-solving and team management. Delivering machine learning systems at scale is an extremely complex endeavour and I strongly believe that we can only succeed if we have a high-trust and collaborative environment that fosters continuous learning and innovation.


How did you develop – and continue to develop – these skills or attributes?

Mainly by being curious and having a positive attitude towards new challenges! Data science is at the crossroads of many disciplines, so there’s always something new to learn there. It is also important to remain humble, as well as honing an ability to sense what will be the data trends in the near future. The technology and capabilities are moving fast and the data landscape can certainly feel very crowded at times, so being able to select the right information becomes critical. Today, I’m prioritising my people skills through coaching and mentoring as these are proving essential in handling complexity and uncertainty.

Is the data tech you have keeping pace with your goals and requirements? Are your providers leading or lagging behind your demands?

The data landscape has certainly seen an explosion of third-party providers in the past years, especially when it comes to out-of-the-box machine learning solutions. Our strategy here is three-fold: building in-house expertise to navigate these complex conversations involving third parties, establishing a clear build v buy framework, and having a solid estimation of the value of our datasets.

Reda Kechouri
has been included in:
  • 100 Brands 2022 (EMEA)