Discover opportunities to improve systems, processes and enterprises through data analytics and automation
Build customer-focused analytics with data scientist peers
Implement solutions to solve sophisticated engineering problems using machine learning, econometrics and operations research techniques
Develop in-depth business and domain understanding
Establish and maintain collaboration with other internal teams to apply cloud and edge-computing capabilities for production and proprietary algorithms
Translate analytics techniques published in peer-review journals and conference proceedings into practical solutions for challenges at hand
Generate reports for decision-making using data visualization tools
Requirements
python
docker
azure
reinforcement learning
etl
signal processing
Proficient in verifying raw data integrity through automation and classic extract-transform-load (ETL) techniques
1+ years of practical experience in predictive maintenance
Bachelor Chemical Engineering, Mechanical Engineering, Electronic / Electrical Engineering, Aerospace Engineering, Mathematics, Data Science, Statistics or other relevant degree
Experience with Docker, Azure DevOps, Agile sprints
Ability and interest in translating highly sophisticated and abstract concepts to internal and external team members with minimum analytics knowledge
Strong interdisciplinary background, combining data science expertise with a solid grounding in traditional engineering fields such as Chemical, Mechanical, Aerospace, or related disciplines.