Vehicle Prognostics - Applied Data Scientist

Ford’s **Electric Vehicles, Digital and Design (EVDD)** team is charged with delivering the company’s vision of a fully electric transportation future. EVDD is customer-obsessed, entrepreneurial, and data-driven and is dedicated to delivering industry-leading customer experience for electric vehicle buyers and owners. You’ll join an agile team of doers pioneering our EV future by working collaboratively, staying focused on only what matters, and delivering excellence day in and day out. Join us to make positive change by helping build a better world where every person is free to move and pursue their dreams. **In this position...** Are you passionate about leveraging modern day data science methodologies/tools to study and predict the degradation or occurrence of a problem in a vehicle component/system? Would you love to accelerate our efforts to build amazing experiences and software products in the Connected Vehicles space - with data? We are seeking top-tier **Applied Data Science** professionals who are data driven, self - motivated and detail oriented to help develop and deliver breakthrough Prognostic Features. **What you'll do...** - **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. **You'll…