Description

You will analyze data to improve the correlation between digital twin models and physical testing for heavy-duty trucks.

This role is on-site.

Responsibilities

• Collect, visualize, and analyze data to enhance digital twin model accuracy against physical testing results.
• Translate complex datasets into engineering presentations that communicate findings and model correlation outcomes.
• Collaborate with engineering and testing teams to develop test plans and interpret data for design recommendations.
• Synthesize simulation results, technical requirements, and historical data to identify functional improvements.
• Coordinate with global strategy teams to refine simulation strategies through rigorous data analysis.

Required Skills

• 5+ years of experience in data analysis, specifically within the automotive or transportation industry.
• Proficiency in Python and SQL for statistical analysis and data manipulation.
• Experience with Database Management Systems (BDMS) and querying data.
• Advanced data visualization skills using PowerBI.
• Strong background in durability and fatigue analysis fundamentals.
• Knowledge of machine learning techniques for data analysis and predictive modeling.
• Ability to apply statistical methods to extract insights and drive data-driven decisions.
• Bachelor's Degree in Computer Science, Engineering, or a related field.

Preferred Skills

• Master's Degree in a related field.
• Experience specifically with heavy-duty vehicles.

Key Skills

Education

Bachelor's Degree in Computer Science