This past week I flew down to Guanajuato, Mexico with the Fin-ML group to participate in the Industrial Problem Solving Workshop. Firstly, what is Fin-ML? Through a partnership with the Institute for Data Valorization (IVADO), Fin-ML (or Machine Learning in Finance) is a program that was created to become a Canadian reference for acquiring specialized skills in the field of innovative machine learning technology in quantitative finance and financial business analytics. In this program, I’ve gained access to a scholarship, training, mentorship, and this international workshop in Mexico. Fin-ML built a partnership with CIMAT, the Centre for Research in Mathematics in Guanajuato, Mexico. This partnership was established to share knowledge regarding data valorization advancements, including artificial intelligence. 


Every year, CIMAT hosts the Industrial Problem Solving Workshop, otherwise known as SPI, and this year their focus turned to data science and AI, so it was a natural fit to invite the Fin-ML group. 10 of us traveled from Montreal to participate. 

On the first day of the workshop, 6 Mexican organizations presented industrial problems and we were free to choose the problem we would like to solve during the week. These organizations included governmental institutions, startups, and large enterprises. I chose to join the group that would be working on predicting property policy cancellations for HDI Seguros, a Mexican insurance company. We spent the week working on advanced churn models which was fascinating considering Breathe Life works directly with large insurers across North America. 

Overall, this was a great experience to be part of and I came out of the week feeling energized about the data science community and having expanded my network of data science professionals. It was great to get to work directly with data scientists in Mexico and learn about the data science problems that their local companies face. Even though Canada and Mexico might seem like very different countries, our local companies all face similar problems and our data scientists are hard at work trying to find solutions to them. 

Many thanks to the Fin-ML and CIMAT teams for making this experience possible!