[NYC] Before Kaggle: From a Business Goal to a Machine Learning Problem

This meetup is sponsored by Bloomberg, who will also be recruiting at the event. Don’t forget to bring a resume!

Instructions: Mention us at security and they will instruct you where to go.

Event Agenda:

6:30 – 7:00 PM: Networking & Food
7:00 – 7:15 PM: Short talk
7:15 – 8:30 PM: Keynote talk by Pierre Gutierrez
8:30 – 9:00 PM: Socializing

Short Talk: Music Recommendations at Spotify by Vidhya Murali

Main Talk Abstract:

Pierre will speak about what happens leading up to a Kaggle competition and how to translate a business need into a machine learning problem. Although one can learn a lot from a data science competition, data scientists need to be able to solve all aspects of a problem. Armed with his experience with Kaggle competitions and as a data scientist at Dataiku, Pierre will talk about topics like creating a target variable, choosing the right metric to optimize and how to correctly evaluate a machine learning model.

Speaker Bio:

Pierre Gutierrez is a senior Data Scientist at Dataiku (www.dataiku.com). He has experience in several topics such as fraud detection, predictive maintenance, recommender systems, smart cities and churn prediction. Pierre likes to apply Data Science tools to real world problems. Dataiku edits Data Science Studio (DSS), a collaborative development platform for data professionals. The product integrates standard data and machine learning tools and technologies, allowing users to build their own data-driven projects.