Data Science at Scale across Industries: Lessons Learned

Data Science projects are very different depending on the use-case and the industry. Several steps are important to make such projects successful and repeatable within a company. Our team has leveraged innovations coming directly from IBM Research together with our platform to tackle these projects together with our clients. This talk will cover some of these approaches and shed some light on how we could make the whole process more transparent for all the business units within the company.

•  Putting Data Science into Production instead of Prototyping
• Business Innovation through structured Data Science Projects
•  Trust and Fairness across the Process

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Maxime Allard – Data Scientist | IBM

Maxime is a Data Scientist with the Data Science Elite Team where he helps clients develop and deploy machine learning algorithms to create business value. Some of his past client engagements revolved around analysing thousands of documents or scaling ML pipelines for better prospect predictions. He joined the DSE team in New York after finishing his Master in Operations Research at Columbia University and after two years he joined the same team in London. Currently, he is dividing his time between being a Data Scientist and doing a PhD at Imperial College London in ML and Robotics to make robots more adaptive and versatile.