Vehicle Prognostics - Applied Data Scientist

About the role

Join Ford’s Electric Vehicles, Digital and Design (EVDD) team to pioneer breakthrough Prognostic Features. As an Applied Data Scientist, you’ll own the process for prognostic feature development from conceptual to feature deployment to production vehicles. You’ll fuse first-principles physics modeling with advanced machine learning to develop hybrid, high-fidelity prognostic models, design and deploy state-of-the-art prognostics models for RUL estimation, and architect edge models in C++ for on-board vehicle ECUs.

Responsibilities

• Own the process for prognostic feature development from conceptual to feature deployment to our production vehicles.
• Pioneer Physics-Informed Machine Learning (PIML) to develop hybrid, high-fidelity prognostic models capturing complex degradation behaviors across EV and ICE powertrains.
• Design and deploy state-of-the-art prognostics models to accurately estimate the Remaining Useful Life (RUL) of critical vehicle subsystems, transforming fleet data into actionable maintenance alerts.
• 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 ECUs.
• 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.
• 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.
• 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.
• 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.
• Partner closely with EV and ICE component subject matter experts to translate deep physical domain knowledge into robust on-board and off-board diagnostics.
• 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.
• 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 practicing statistical methods and their accurate application, including ANOVA, principal component analysis, correspondence analysis, k-means clustering, factor analysis, multivariate analysis, Neural Networks, causal inference, Gaussian regression, etc.
• 3+ years of 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 tools (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

Qualifications

• 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+ years of experience in application of statistical and machine learning methods, including 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

Skills

• Statistical methods and their accurate application
• Python (and related modules), SQL
• Embedded controls, onboard Diagnostic, Sensor Processing, General First Principles Physics Modeling and simulation using numerical computational tools (e.g. MATLAB, ATI, Simulink)
• Digital Signal Processing (DSP) data structures, algorithms, and software engineering principles
• Statistical and machine learning methods
• Open-source data science technologies such as Python, R, Spark, Hadoop, etc.
• 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

Benefits

Immediate medical, dental, vision and prescription drug coverage
Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
Vehicle discount program for employees and family members and management leases
Tuition assistance
Established and active employee resource groups
Paid time off for individual and team community service
A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
Paid time off and the option to purchase additional vacation time.

Pay

This position is a range of salary grades 6-8 and ranges from $85,400-$160,000.

Schedule

This position is hybrid. Candidates who are in commuting distance to a Ford hub location may be required to be onsite four or more days per week.