Data Scientist

KEY EXPECTED ACHIEVEMENTS

The business need is understood and formalized in a descriptive datasheet or specifications
The methods are clearly selected by their theoretical bases, advantages and drawbacks
The data, its relevance and its source are described and prioritized
The data are prepared (cleaned, enriched, mapped, agregated, ) and the approach is documented
The data analysis is implemented and documented
Machine Learning pipeline are built, tested and automatized for deployment
The algorithms and models (descriptive and/or predictive) are developed and the approach is documented
The results of the analysis or the models are presented to the customers with data visualisation tools indicating the performances and the limits. The source code is delivered and explained if necessary
Models are validated and decisions deployment are taken, from roll-out-strategy
Peer reviews are organized to ensure quality of delivering.