Suju Rajan on how innovation and machine learning are the core of Criteo

June 23, 2017

Tell us about your background and how you ended up at Criteo.

I started out as an intern building behavioral targeting models in Yahoo Labs and got to experience a variety of exciting applications of machine learning from search relevance to personalized recommendations. In parallel, on the management side, I grew from being an intern to directing a research team.

I had friends who worked at Criteo, and I was very aware of the importance given to machine learning here. When I was offered the opportunity to create and grow a research group, I had to sign on to the challenge.

What about our product most excites you or makes you proud to work here?

Even when interviewing at Criteo, I was very impressed by the forward and lateral view that the engineering and product teams had in wanting to constantly identify new opportunities to invest in. Criteo is the only success story in the space of publicly traded ad tech companies. The interviewers could have sold me on just the merit of our market position. Instead, the focus was on what we could do better, what opportunities we are missing out on, and where we could go with the data assets we have. This urge to stay ahead of the game, to constantly innovate and the laser-sharp focus on performance is what makes me proud to work here.

What makes working at Criteo different from your other experiences?

The strong mathematical and statistical training that our engineers and product managers have is a differentiator. It is easy to have an in-depth conversation on what the research team is working on and to get meaningful feedback on how that fits within the engineering and product roadmap. I have even had very insightful conversations with folks on the sales team on how the research team can help translate their insights from client conversations to factor into our machine-learned models. How cool is that? The whole company has a strong sense for what it means to be data-driven.

What’s your favorite project you’ve worked on?

We kicked off an internal machine learning “sabbatical” for our engineering counterparts — the Machine Learning Bootcamp. Members of the research team offer a set of classes that focus on the theoretical aspects of machine learning algorithms from simple categorization to reinforcement learning and, of course, deep learning. Our Bootcampers then sit alongside the research team to deep-dive into a machine learning research project. It has been exciting to see the enthusiasm with which the Bootcamp has been received, and we have even reached the point where our Bootcampers are publishing papers based on their projects.