Hareesh Bahuleyan, an Applied Scientist from our Size & Fit team, chats to us about ensuring a great shopping experience while benefiting the environment.
How did you get into working with machine learning and data science? What interests you the most about it?
During the final year of my undergraduate studies, I had the opportunity to work on real-time traffic data captured by sensors. This project introduced me to the world of data science, as I carried out some exploratory data analysis and built my first machine learning model for route travel time prediction. I realized how data could be used to improve people’s lives. Today, machine learning has become an integral part of our lives and is being used in domains ranging from search engines to drug discovery. I am always excited to hear about the new potential applications of this technology.
Can you briefly describe the technology you are helping to develop at Zalando?
I am a part of the Size & Fit team. We optimize size advice for our online shoppers. Due to the lack of a physical try-on experience, a common customer problem is having to return items because the size or fit was not right. As Applied Scientists, we develop algorithmic solutions that guide customers to buy the right size the first time, which creates a better shopping experience for them. Solving this problem presents a huge opportunity — not only reducing the logistics costs of returns for Zalando but also benefiting the environment by minimizing the CO2 emissions from the return logistics.
How do you measure the success of your work?
As an Applied Scientist in a product team, I am responsible for driving the development and experimentation of algorithmic solutions. Ultimately, we are successful when these solutions progress beyond the experimentation stage, and can be rolled out to our customers in a way that they improve key metrics — which in our case is a reduction in size-related returns. At Size & Fit, we are also very keen to push the state-of-the-art in our domain and publish our work at scientific conferences.
What gives you the most satisfaction in your job?
Firstly, customer impact: the fact the methodologies and solutions that we develop have the potential to impact millions of Zalando customers. Secondly, scientific challenges: providing size advice with machine learning is definitely an unsolved problem and there are always new challenges to explore.
What is it like to work in such a multi-disciplinary team?
Yes, our team consists of Applied Scientists, Engineers, Product Managers, Product Designers, Business Developers and so on. Working in such a multi-disciplinary and international team where people come from diverse backgrounds, you get to learn a lot from one another. Moreover, this enables us to look at the same problem from multiple perspectives, which is key to building highly innovative products.
What should a new joiner to your team expect?
For new joiners to our team, we have a structured onboarding program where you work on a specific product-based research problem. You would be assigned mentors who would guide you, not only on project-related topics, but also on navigating through data and infrastructure at Zalando. This way, you have ample time to gradually integrate and understand our ways of working. People in our team are very approachable and you can expect to receive some tips even on topics such as moving to Germany, for instance, if you are relocating from abroad.
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