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Hub-site Data Engineer - Data Scientist Manager
- Provides technical leadership in supporting OU innovation projects.
- Acts as a key enabler in leading and delivering results against challenges.
- Works with innovation team looking at new platforms, machine control, data processing and analytics. Helps develop capability in others.
- Develop and plan required analytic projects in response to business needs. Leverage data science tools to solve the toughest process problems in the region.
- Develop new analytics/predictive/prescriptive modeling methods and/or tools as required. Propose prescriptive analytic models to build robust and fault-tolerant process control strategies to reduce Operations Effort and improve product quality
- Work with process/equipment experts and application developers to identify data relevant for analysis.
- Contribute together with process/equipment owners and ITOT to the development and evolution of data models for analytical capabilities. Own data model and maintenance and development for hub-site
- Develop and maintain data pipelines for the hub-site
- Contribute to define work processes to deploy and maintain predictive/analytical modeling architectures, modeling standards, alarming and reporting, and data analysis methodologies.
- Conduct external focus research to drive suggestions on analytical modeling products, services, protocols, and standards that might support and speed-up the smart manufacturing journey.
- Identify, diagnose, and resolve prognostics model performance issue.
- Leverage Reliability Engineering enhance with data science to develop new solutions to reduce losses.
- Bachelor's degree in Computer Science, Computer / Systems / Engineering, Business / Management Information Systems, Programming / Software Development, Operations Research or Statistics and want to build on this with continuous learning and bringing the “outside in”. Experience in Manufacturing is a bonus.
- Sufficient business knowledge to understand what data is important, when findings are relevant, and how to exploit data to make decisions. Strong familiarity with data preparation and processing.
- Strong analytical statistical and mathematical modeling capabilities to form hypothesis and to collect, explore, and extract insights from structure and unstructured data to rationally transform raw data into useful information to improve process/equipment operation and maintenance.
- Efficient use of software for data visualization, statistical analysis and ML, e.g., Python, R, SAS, Azure ML, Matlab, PowerBi, Tableau, and familiarity with functional programming and scripting languages, e.g., .NET, VB, C++, Python, Matlab.
- Understanding data architecture concepts, i.e., relational database structures, data warehouse, big data management, data queries, etc.
- Technical understanding of mechatronic systems / first principles (combining principles of process, mechanic, electrical, control and systems engineering)
- Experience in advance analytics, big data, machine learning.
- Experience in designing visualization to maximize user experience and effective decision making
- Innovation Mindset – Ability to assess business operations and identify opportunities, then drive business case
- Experience in data engineering using Azure Data Factory, Databricks, SQL, Knime
- Experience in Agile project (SCRUM, Kanban) and operate in DevOps, CI/CD environments
- IT Security & Risk – In touch with modern IT security risks and principles.
- Proven ability to handle concurrent priorities, strong written and verbal communication skills to influence others and act collaboratively across functions
- Teamwork attitude with clear technical communication skills to explain data models and data analytic solutions to other technical peers and to leadership teams.
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