Senior Data Science Consultant

Job Overview

The Digital Manufacturing EMEA team is part of the Continuous Improvement EMEA Team, focusing on waste elimination and efficiency improvements in all its aspects, with a significant contribution on the overall financial performance of Automotive EMEA.
The Continuous Improvement EMEA team reports to Operations Automotive EMEA.
By providing new generation digital solutions to our Operational teams across EMEA, our team creates full transparency and speed in the ongoing digital & lean transformation(s), which will enable all involved functions to leverage data to develop business insights and roadmap updates based upon clearly arranged and understandable data.
Due to the extended scope, focusing also on the connecting processes from Supplier(s) to Manufacturing ending at the Customer(s) (internal and external) we will function as ‘the’ differentiator for our Company.
This ‘Data Scientist Automotive EMEA’ will report to the Sr. Mng. Digital Manufacturing EMEA. 
 

Responsibilities:

• Lead projects and work together with our Automotive EMEA Operations plants, internal (IT, TEIS, Corporate Technology, CoE, SC, ..) and external partners in the area of Digital Factory (Applications, ML, AI, DL), aligned with Operations Automotive EMEA Strategy.
• Lead Digital Factory improvement projects (Enhancements) of existing applications together with internal / external supplier and customers (project management) until the level of RTD (Ready To Deploy)
• Use a combined knowledge of computer science and applications, modelling, statistics, analytics and math to solve problems
• Identify and interpret data sources, manage large amounts of data, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data
• Develop, Validate and Deploy (including Training and Coaching of the users) until the level of RTD (Ready To Deploy), Digital Factory Applications, ML models, contributing to waste elimination and efficiency improvements within all Operations functions (Manufacturing, Quality, SC, LOG, ….), this together with our internal and external partners.

Desired Candidate Profile:

• Good understanding of a technology driven manufacturing company.
• Knowledge of the core manufacturing technologies: Stamping, Plating, Assembly, Molding.
• Experienced with data visualization tools like PowerBi, Tableau, ThingWorx,..
• Good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Development experience in programming languages R, Python,..
• Knowledge, experience in JAVA scripting
• Excellent pattern recognition and predictive modelling skills.
• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, neural networks and deep learning etc.
• Experience in technologies like, Jupyter Notebooks, Numpy, Pandas, Seaborn, Scikit-learn, PyTorch and TensorFlow.
• Exposure to recent developments in Deep Learning domain.
• Database skills, on-premises and cloud based.
• Experience in AWS is an advantage, functional knowledge of AWS platforms such as Sagemaker, S3, and RedShift.
• Excellent presentation skills in both spoken en written English.
• The ability to translate, explain and bring, complex Data Science topics to an easy understandable level, speeding up acceptance level in general.
• Bachelor or master in Data Science
• Team player with a solution focused problem-solving mentality.
• Change agent with high Execution level, including training and coaching talents.
• Passioned about the combination Technology, Data, Analytics and Statistics with link to Quality, Product and Process Engineering
• Inspired by and searching actively for new technologies and methods.
• Driving TE Values : Integrity, Accountability, Teamwork, Innovation
• Working knowledge of connectivity to databases (using various packages like sqlite3,pyodbc, postgresSQL,AWS Redshift)  and working with Big Data on languages like Python/R
• Working knowledge of Image classification which may include
• Algorithms like CNN, SVM, k-Nearest neighbor, MLPs and SGD (Stochiastic gradient Descent) would be a welcome addition etc.
• Data Preprocessing - Image resizing, Gaussian Blur
• Data normalization - PCA (Principal Component Analysis) etc
• Working experience in AWS and/or MS Azure for model building and deployment using pre-defined services
• End-to-end implementation experience of ML models - using server-based callbacks
• Real-time computer vision algorithms
• Exposure to vision systems
• Deep learning techniques
• Auto ML tools
• Transfer learning to utilize model accuracy and feedback loop implementation

Competencies