Job Summary
We are seeking a highly motivated Scientist / Research Engineer to develop and deploy advanced prognostics and predictive maintenance solutions for vehicle systems. The ideal candidate will leverage data science, machine learning, physics-based modeling, and signal processing techniques to predict component degradation and estimate Remaining Useful Life (RUL) for automotive applications.
Key Responsibilities
- Develop prognostic and predictive maintenance algorithms using vehicle and connected vehicle data.
- Build machine learning and physics-informed models to analyze component health and degradation.
- Process and analyze large datasets using Python, SQL, and cloud platforms.
- Develop fault detection and anomaly detection solutions for vehicle systems.
- Perform modeling and simulation using MATLAB/Simulink.
- Collaborate with cross-functional teams to deploy solutions into production vehicles.
- Support embedded software implementation in C++ environments.
Required Skills
- Python
- SQL
- C++
- Data Science & Machine Learning
- MATLAB / Simulink
- Algorithms & Statistical Analysis
- Google Cloud Platform (Google Cloud Platform) or other cloud environments
- Signal Processing (DSP)
- Predictive Analytics & Prognostics
Required Qualifications
- Master''s degree in Mechanical Engineering, Electrical Engineering, Computer Science, Computer Engineering, Mathematics, Physics, or related field.
- 4+ years of experience applying statistical and machine learning techniques.
- 3+ years of experience with Python and SQL.
- Experience with predictive modeling, sensor data analysis, and vehicle diagnostics.
- Strong analytical and problem-solving skills.
Preferred Qualifications
- PhD in a related technical field.
- Experience in Automotive, Connected Vehicle, EV, Controls, Robotics, or Prognostics & Health Management (PHM).
- Knowledge of Spark, Hadoop, ATI, ETAS, and embedded systems.
- Experience with machine learning techniques such as PCA, ANOVA, Clustering, Neural Networks, Time Series Forecasting, and Causal Inference.