Turns business questions into analytical and data science problems 

Defines success metrics and evaluation criteria aligned with business outcomes  

Analyze and explore structures and unstructured data to identify patterns, trends and anomalies 

Build production grade automation workflows 

Assess data quality, completeness, bias and limitations 

Identify additional data sources needed to improve analysis or models 

Perform data transformation for modelling purposes  

Model development and validation 

Interpret model results and analytical findings in business terms 

Communicate findings clearly to non-technical stakeholders 

Document assumptions, limitations and risks of the model 

Works with operators and production/quality technicians to understand objectives, constraints and decision needs

Work with data engineers /Digital team to operationalize data preparation when needed.

Works with data scientists and CAE experts to understand the value of the project and how it links with the roadmap.   

8 - 11 yrs of Experience  

Masters or PhD in Data science/ Applied mathematics/Statistics 

Experience working with large-scale data sets in an industrial environment 

Strong fundamentals of linear algebra, Calculus, probability and statistics. 

Solid understanding of distributed systems concepts, data partitioning and parallel processing 

Hand-on experience using databricks for data exploration, analysis, feature engineering and model training 

Strong proficiency in Apache spark (dataframes, SQL, ML lib concepts), for large scale data processing,  with hands-on experience in distributed model training using Pytorch or tensor flow.  

Databricks notebooks (Python/SQL) 

Experience in Knowledge Graph a plus 

Strong proficiency in pandas and numpy for exploration, feature engineering and analytical workflows 

English & French are both mandatory