Internship Data Science Physics Jobs in Michigan

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.

You'll have...

• 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.

You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!

As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder...or all of the above? No matter what you choose, we offer a work life that works for you, including:

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. 

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

Final determination of salary grade will be based on candidate's skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.

For more information on salary and benefits, click here: https://fordcareers.co/GSR

Visa sponsorship is not available for this position.

Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660.

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. 

#LI-Hybrid     #LI-MG3 

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.