Role and Responsibilities
- Identify relevant internal and external data sources and sets to mine across multiple sites for meeting business requirements. Collect large structured and unstructured datasets and variables.
- Enhance and optimize data collection procedures to include relevant information for building analytical systems.
- data mining techniques and perform statistical analysis using state - of - the - art methods in sandbox and test environments. Devise and utilize algorithms and models to process big data.
- Perform data and error analysis to improve models, clean and validate data for uniformity and accuracy. Create automated anomaly detection systems and constant tracking of performance.
- Analyze data for trends and patterns. Interpret and contextualize data to meet requirements specification. Infuse additional data sources as needed.
- Develop, (re - )train and maintain Machine Learning / AI models to process big structured / unstructured data analytics streams.
- Create consumption - ready predictive models that can be leveraged by manufacturing stakeholders for improving operations, efficiency and predictive analytics (such as predictive quality, predictive maintenance, traceability and energy optimization). Document and present analysis results providing valuable business insights. Craft technical requirements specification for development purposes.
- Deploy predictive analytics solutions for manufacturing industry into production drive end - toend process from creating and tuning algorithms to operationalizing the model into workflows to measure impact.
- Collaborate with global and local IT teams incl. cloud architect(s), big data architect(s), DevOps resources and others to form, deliver, support and continuously improve digital ecosystem and integrated platforms.
- Communicate solutions for data analytics to stakeholders, drive global adoption across business and implement improvements to operational systems (Continuous Improvement Process).
Qualification and Experience
- Overall experience as Data Scientist in manufacturing industry.
- Automotive industry experience a plus.
- Has worked in a global environment and cross - functional teams. Strong communication and presentation skills.
- Proven experience with a range of techniques, tools and methods related to data modelling, text analytics and NLP (Natural Language Processing)
- Experience with predictive models especially with predictive maintenance, predictive quality and traceability.
- Fluency in Python and R. Proficient in algorithm development in Python ecosystem.
- Hands - on experience using a variety of analytical methods (EDA, Machine Learning - supervised, unsupervised).
- Knowledge and understanding of statistical, predictive modelling techniques and algorithms, incl. Neural Networks, text / data mining, LDA, entity extraction, classification, association rules, hashing, similarities, GLM/Regression, Random Forest, Gradient Boosting, SVM, etc.
- Understanding of Amazon and Microsoft Cloud IoT platforms and toolsets.
Certifications
- SAS Certified Big Data Professional / Data Scientist
- Cloudera Certified Professional Data Engineer