Falabella.com is the largest e-commerce in Latin America. The amount of data available makes any data scientist face new challenges and opportunities for learning and helping the business.
From 2016 to 2018 (sporadic work) I developed and implemented machine learning systems for Falabella. Working as a Data Scientist and a Web Intelligence Engineer, I learned how to deploy machine learning models and their outputs. My team also faced many challenges on data-architectures that we helped to solve.
I led a data scientists team to outperform the user experience and findability at Falabella.com from a machine learning perspective. At the same time, I was part of the team that evaluated and defined the new Big Data technologies and protocols at the company. I also spread a data-driven mindset through the company and shared my scientific knowledge with my colleagues.
It was a great honor and opportunity working with them parallel to academia.
My main contributions were:
During 2016 and 2017 I developed two consulting projects for the Customer Intelligence department of Cencosud.
The main challenge was to understand the buyers' behavior and the business insights.
Footwear recommender system
I developed the recommender system behind this app (only available for mobile devices).
Falabella had about 4000 sandals to sell, but its customers had no enough time to scroll all their pages. I developed a recommenders system that tries to converge as fast as possible to the best product for each customer.
This app was launched as an MVP during 2018.
Using Falabella.com navigation data, I implemented a model that recommends words to specify search queries. This way the clients learn how to use the search engine better and find their products faster.
This works parallel to the type-ahead functionality, which I also helped to set up.
This tool was launched during 2018.
I left Falabella before that all my team's projects were fully deployed. Here I specify some of these projects:
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