Vehicle Prognostics Data Scientist - Edge AI & RUL

• Own the process for prognostic feature development from conceptual to feature deployment to our production vehicles.

• Pioneer Physics-Informed Machine Learning (PIML):
Fuse first-principles physics modeling with advanced machine learning to develop hybrid, high-fidelity prognostic models that capture complex degradation behaviors across both EV and ICE powertrains.

• Architect Prognostics & RUL Frameworks:
Design and deploy state-of-the-art prognostics models to accurately estimate the Remaining Useful Life (RUL) of critical vehicle subsystems, transforming noisy fleet data into actionable maintenance alerts.

• Deploy Edge Models in C++:
Translate complex predictive models into highly optimized, low-latency C++ code, bridging the gap between cloud-based data science and resource-constrained on-board vehicle electronic control units (ECUs).

• Harness High-Frequency Signal Processing:
Architect custom Digital Signal Processing (DSP) pipelines and time-series analytics to extract clean, high-frequency physical signatures from multi-sensor vehicle networks, isolating early-stage wear patterns before they manifest as failures.

• Design Multi-Sensor Fault Detection & Isolation (FDI):
Develop and validate intelligent, multi-sensor anomaly detection frameworks capable of real-time Fault Detection and Isolation (FDI) to ensure vehicle safety, system redundancy, and fault-tolerant control.

• Apply Statistical Causal Inference:
Leverage advanced statistical methods (including causal inference, multivariate analysis, ANOVA, and PCA) to differentiate between mere correlation and true physical root causes of component degradation across massive, connected vehicle fleets.

• Own the End-to-End Pipeline (HIL to Production):
Direct the entire prognostic lifecycle—moving seamlessly from mathematical conceptualization and simulation in MATLAB/Simulink to physical validation on Hardware-in-the-Loop (HIL) benches, prototype vehicles, and ultimately to production vehicle deployment.

• Synthesize Deep Subsystem Domain Knowledge:
Partner closely with EV and ICE component subject matter experts to translate deep physical domain knowledge (thermal, mechanical, chemical, and electrical) into robust on-board and off-board diagnostics.

• Build Scale with Big Data & Calibration Tools:
Ingest and process large-scale telemetry data using Python, SQL, Spark, and Hadoop, while leveraging industry-standard calibration tools (such as ATI and ETAS) to fine-tune algorithms for real-world driving environments. Interact with subject matter experts to understand component/system functions, leverage existing connected vehicle data to model on-board and off-board prognostics algorithms.

• Operate cross-functionally to ensure successful code implementation on production vehicles.

Requirements

• Bachelor's in Mechanical, Electrical, Computer Science, Computer engineering, Physics, Mathematics or related fields or a combination of education and equivalent experience
• 4+ years of experience of practicing statistical methods and their accurate application e.g. ANOVA, principal component analysis, correspondence analysis, k-means clustering, factor analysis, multi-variate analysis, Neural Networks, causal inference, Gaussian regression, etc.
• 3+ Experience with Python (and related modules), SQL
• Experience with embedded controls, onboard Diagnostic, Sensor Processing, General First Principles Physics Modeling and simulation using numerical computational tool (e.g. MATLAB, ATI, Simulink)
• Experience with Digital Signal Processing (DSP) data structures, algorithms, and software engineering principles
• Self-motivated, strong analytical, excellent interpersonal and communication skills required
• ** Even better, you may have...**
• Master's or PhD in Mechanical, Electrical, Computer Science, Computer engineering, Physics, Mathematics or related fields or a combination of education and equivalent experience
• Experience in Dynamic Systems, Control, Robotics, Prognostics and Health Management
• Familiarity working with Automotive prognostics feature development using connected vehicle data.
• 2+ Experience in application of statistical and machine learning methods e.g., ANOVA,…