Engineer Data (Multiple Positions) (REF291620F)

Bosch Group•Charleston, SC

9d•Hybrid

About The Position

Robert Bosch LLC seeks Engineer Data (Multiple Positions) at its facility located at 8101 Dorchester Road, Charleston, SC 29418. Provide data to end users by creating and maintaining data pipelines allowing them to do data analytics and data mining. Create and maintain optimal data pipelines for test and experimental data. Work with data warehouses, data lakes and cloud-based environments. Extract, transform and load the data from a wide variety of sources using Python, SQL, and/or MS Azure. Assemble and analyze large, complex datasets from different sources that meet business requirements. Build analytics tools that utilize data pipelines to provide actionable insights into development and manufacture process improvements, operational efficiency, testing supervision, test evaluation. Work with design, testing and manufacturing engineers to assist with data-related issues and support data infrastructure needs, including predictive maintenance and performance evaluation. Work with an international team of data and analytics experts to strive for greater functionality in data systems and digitalization efforts. Monitor release testing progress and implement new tracing metrics, applying machine learning and visualization methods to classify and evaluate test data. Identify, design and implement internal process improvements, including physics-based modeling and automating analysis pipelines. Build processes supporting data transformation and data structures, demonstrate through Python-based pipelines for test and experimental data. Ability to manipulate, process and extract value from large connected and disconnected datasets in both experimental and production environments.

Requirements

Bachelor’s degree or foreign equivalent in Mechanical Engineering, Aerospace Engineering, Computer Science, Informatics, Information Systems or a related field.
3 years of experience as a Mechanical Engineer, Data Scientist or in an occupation involving data processing, designing mechanical components, testing and validating components.
Employment experience with cloud environments such as Azure and Databricks, and proficiency in Python for large-scale data analysis in development, testing and production environment.
Employment experience with PySpark or Scala, and Python for transformation, modeling, and analysis of high-volume test and experimental data coming from different sources like test benches, vehicle data, simulations and production equipment.
Employment experience collecting, processing, and analyzing data from multiple internal and external sources to perform root cause analysis related to vehicle dynamics and system performance, identifying improvement opportunities, developing data driven solutions, and validating results through numerical simulations and test data correlation.
Employment experience performing root cause analysis on production-related troubleshooting through vibration, durability, and hydraulic system data.
Employment experience with machine learning algorithms for classification, predictive maintenance, and test data analysis.
Employment experience manipulating process and extract insights from large experimental and production datasets.
Employment experience with Hydraulic Systems, Tableau, Power BI, Qlik, PySpark, Scala, Python, Azure, Databricks, Siemens NX and Teamcenter, MS Project, MATLAB, Ansys Fluent, and Abaqus.

Responsibilities

Provide data to end users by creating and maintaining data pipelines allowing them to do data analytics and data mining.
Create and maintain optimal data pipelines for test and experimental data.
Extract, transform and load the data from a wide variety of sources using Python, SQL, and/or MS Azure.
Assemble and analyze large, complex datasets from different sources that meet business requirements.
Build analytics tools that utilize data pipelines to provide actionable insights into development and manufacture process improvements, operational efficiency, testing supervision, test evaluation.
Work with design, testing and manufacturing engineers to assist with data-related issues and support data infrastructure needs, including predictive maintenance and performance evaluation.
Work with an international team of data and analytics experts to strive for greater functionality in data systems and digitalization efforts.
Monitor release testing progress and implement new tracing metrics, applying machine learning and visualization methods to classify and evaluate test data.
Identify, design and implement internal process improvements, including physics-based modeling and automating analysis pipelines.
Build processes supporting data transformation and data structures, demonstrate through Python-based pipelines for test and experimental data.
Manipulate, process and extract value from large connected and disconnected datasets in both experimental and production environments.