Durability Analytics Engineer — Early Career

Watershed Ventures; Irvine (CA); On-site; USD 97,000 - 121,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Employee stock purchase program

Paid vacation & sick leave

Job summary

Rivian is seeking a Durability Analytics Engineer to predict and test loads and stresses on vehicles, using fleet data and vehicle test data to drive durable, data-driven engineering solutions. You will develop models that estimate loads, fatigue damage, and vibrations across components and vehicle variants.

You will collaborate with simulation, test, and data analytics teams to automate processes and standardize durability target development while leveraging machine learning and physics-based

Qualifications

• Bachelor's degree in Mechanical Engineering, Aerospace Engineering, Computer Science or similar.
• 1-3 years experience in data analytics for automotive or related engineering applications preferred.
• Experience with Python (Pandas, Numpy), PySpark, Matlab, and SQL applied to big datasets.
• Demonstrated expertise with various machine learning techniques applied to automotive use data, test data, or simulation data.
• Experience in fatigue life estimation (Rainflow counting, Miner’s Rule) and Weibull analysis.
• Strong analytical writing and clear, concise presentations.

Responsibilities

• Fleet Data Analytics: Develop fleet data analyses mapping travel, driving, road infrastructure, and vehicle usage across the US and other markets.
• Build classification models to identify usage archetypes, usage frequency, and their correlations.
• Data-Driven Modeling and Standardization: Develop and validate machine learning models to estimate vehicle loads, drive events, fatigue damage, and vibration spectra from CAN, wheel-force transducer, or accelerometer data.
• Develop models to standardize vibration spectra at different vehicle locations across variants and platforms.
• Learn from simulation and test data to build a library of regression models for variance in component loads and fatigue damage with design parameter variations.
• Automate processes such as block cycle development and accelerated duty cycle development from real-world data; apply ML to reduce CAE test cases and runs.

Skills

Data analytics

Machine learning

Automotive engineering knowledge

Python

Big data handling

Education

Bachelor's degree in Mechanical/Aerospace/CS

Tools

Python (Pandas/Numpy)

PySpark

Matlab

SQL

Job description

Rivian is seeking a Durability Analytics Engineer to predict and test loads and stresses on vehicles, using fleet data and vehicle test data to drive durable, data-driven engineering solutions. You will develop models that estimate loads, fatigue damage, and vibrations across components and vehicle variants.

You will collaborate with simulation, test, and data analytics teams to automate processes and standardize durability target development while leveraging machine learning and physics-based