Applied Data Scientist – Vehicle Prognostics

Jobtailor; Dearborn (MO); On-site; USD 110,000 - 160,000; Full time

Job summary

Jobtailor seeks a skilled prognostics engineer to lead end-to-end development of predictive health models for vehicle powertrains. You will fuse physics-based modeling with machine learning, deploy optimized C++ edge code on ECUs, and design DSP and time-series analytics to detect wear early.

Collaboration across teams and hands-on HIL validation are essential. Applicants should have a strong background in statistics, Python/SQL, embedded systems, MATLAB/Simulink, and experience with automotive

Qualifications

• Bachelor's in Mechanical, Electrical, Computer Science, Computer Engineering, Physics, Mathematics or related field or equivalent experience.
• 4+ years applying statistical methods such as ANOVA, PCA, clustering, multivariate analysis, neural networks, causal inference.
• 3+ years experience with Python and SQL.
• Experience with embedded controls, onboard diagnostics, sensor processing, and physics-based modeling (MATLAB/Simulink).
• DSP data structures and software engineering principles.
• Strong communication, analytical and teamwork skills.
• Masters/PhD and experience in dynamic systems, control, robotics and health management are a plus.
• Open-source data science tools (Python, Spark, Hadoop) and automotive software with C++ in embedded environments (ATI/ETAS familiarity).
• Excellent verbal and written communication; credible in time management and problem solving.

Responsibilities

• Own prognostic feature development from concept to deployment in production vehicles.
• Pioneer Physics-Informed ML to create high-fidelity prognostic models for EV and ICE powertrains.
• Architect prognostics and RUL frameworks for reliable maintenance alerts.
• Deploy edge models in C++ for on-board ECUs with low latency.
• Develop DSP pipelines and time-series analytics for early wear patterns.
• Create multi-sensor fault detection and isolation frameworks for safety and redundancy.
• Apply causal inference to distinguish root causes of degradation across fleets.
• Lead end-to-end pipeline from MATLAB/Simulink simulations to HIL benches and production deployment.
• Translate deep domain knowledge into robust onboard and offboard diagnostics.
• Ingest large telemetry data with Python/SQL/Spark/Hadoop and calibrate algorithms with ATI/ETAS tools.
• Collaborate cross-functionally to ensure successful production implementation.

Skills

Analytical thinking

Interpersonal skills

Problem-solving

Self-motivated

Education

Bachelor's in Mechanical, Electrical, Computer Science, or related fields

Master's or PhD (preferred)

Tools

Python

SQL

MATLAB/Simulink

C++ (embedded)

DSP

Spark

Hadoop

ATI/ETAS calibration tools

Job description

Responsibilities

• 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, PCA, clustering methods, causal inference, time series forecasting, random forest, multi-variate analysis, neural networks, etc.
• Expertise in open-source data science technologies such as Python, R, Spark, Hadoop, etc. acquired through college course work, online training and certification or project development.
• Experience in software development for automotive controls with hands on experience using MATLAB for large scale data and understanding of programming fundamentals and experience with C++ programming in embedded environments. ATI and ETAS calibration tool familiarity
• Excellent verbal and written skills. Highly credible in organizational, time management, decision making and problem-solving skills.