What you'll do:
Eaton Corporation's Center for Intelligent Power has an opening for a Data Scientist who is passionate about his or her craft. The Data Science Engineer will be involved in the design and development of ML/AI algorithms to solve power management problems. In addition to developing these algorithms, the Data Science Engineer will also be involved in successful integration of algorithms in edge or cloud systems using CI/CD and software release process.
• The candidate will demonstrate exceptional impact in delivering projects in terms of architecture, technical deliverables and project delivery throughout the project lifecycle. The candidate is expected to be conversant with Agile methodologies and tools
• Experience on data analysis, ML/AI model development and related development environment that enables ML/AI algorithms for use on various processors or systems
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• Work with a team of experts in deep learning, machine learning, distributed systems, program management, and product teams, and work on all aspects of design, development and delivery of end-to-end pipelines and solutions.
• Development of technical solutions and implement architectures for project and products along with data engineering and data science teams
• Participate in architecture, design, and development of new intelligent power technology products and production quality end to end systems
Qualifications:
- Master's degree in Data Science or Ph.D. (ongoing) in Data Science
- 2+ years of progressive experience in delivering technology solutions in a production environment
- 2+ years of practical data science experience in the application of statistics, machine learning, and analytic approaches with a proven track record of solving critical business problems and uncovering new business opportunities
- 2 years working with customers (internal and external) on developing requirements and working as a solutions architect to deliver
- Masters in Data Science or Pursuing PhD in Datascience or equivalent
Skills:
- Good Statistical background such as Bayesian networks, hypothesis testing, etc.
- Hands on development of Deep learning and Machine learning models for Engineering applications like electrical/electronic systems, energy systems, data centers, mechanical systems
- Hands on experience on ML/DL models such as time series modeling, anomaly detection, root cause analysis, diagnostics, prognostics, pattern detection, data mining, etc.
- Knowledge on data visualization tools and techniques
- Programming Knowledge - Python, R, Matlab, C/C++, Java, PySpark, SparkR
- Azure ML Pipeline, Databricks, MLFlow
- Hands on optimization techniques like dynamic programming, particle swarm optimization, etc.
- SW Development life-cycle process & tools
- Agile development methodologies and concepts including handson with Jira, bitbucket and confluence.