At the crossroads of several disciplines, data science relies on methods and algorithms to obtain information and knowledge from both structured and unstructured data. Still unknown a few years ago, the job of a data scientist is evolving very quickly; on top of working on data sets, plus training and model refinement, ops and cloud-based skills are now a key part of the role.
To assess this evolution and compare our experience against that of the wider market, we interviewed Christophe Thibault, an external data scientist from outside Devoteam A Cloud with whom we have previously collaborated on a client project.
Christophe Thibault, Data Scientist
After a research career across energy-related subjects, data scientist consultant Christophe Thibault changed his career path to focus on data processing and become a data scientist. After three years in the profession, he shares his thoughts on the data science profession, its daily challenges, the variety of use cases, problems and the main tools used.
In this chapter we arge going to discuss in details about differnt topics from Christophe’s perspective such as:
- Which skills are most useful to you in your work?
- How would you define the role of a data scientist?
- What kind of tools do you use?
- What tools or features are missing today?
- What are the business use cases?
- How often are production models updated?
- What about models in the R&D or proof of concept (POC) phases?
- What are the main challenges?
- What is a typical day in the life of a data scientist?
–
If you are interseted in learning more about those topics don’t hesitate to download the E-Book
