SKILLS
Soft skills
Combination of business focus analytical and problem solving skills in order to quickly
define a datadriven solution within different initiatives
Ability to work within a team and with a proactive attitude
Ability to communicate and popularize technical topics
Tech skills
Main skills
Knowledge of at least one of some of the main Big Data frameworks and platforms: Spark
Databricks Snowflake
Programming in Python (Numpy Pandas Scikitlearn )
Mastery of basic Devops tools (Versionning Git Docker CI/CD concepts e.g. Gitlab CI
framework)
Mastery of ML Ops and ML Engineering frameworks (Experiment trackers e.g. MLFlow
Orchestrators e.g. Airflow Kubeflow Sagemaker)
Knowledge of a variety of machine learning techniques (clustering decision tree learning
artificial neural networks ...) and their realworld advantages/drawbacks. An experience in
models robustness analysis and monitoring in production is highly appreciated.
Knowledge of advanced statistical techniques and concepts (regression properties of
distributions statistical tests )
Knowledge of Monitoring model algorithm and A/B testing method
Other skills
AI: mastery in one AI field such as Natural Language Processing or Computer Vision is
appreciated