What you'd actually do

Design, build, scale, and maintain RO duration forecasting and predictive models, leveraging operational and market inputs such as repair types, fuel types, customer segments, product quality, service parts availability, dealer capacity, and macroeconomic indicators.

Establish and ensure high standards of data quality, model governance, and validation testing throughout the entire analytics development lifecycle.

Lead deep-dive and root-cause analyses on vehicles with extended repair times to identify early indicators of anomalies, isolate inefficiencies, and uncover actionable insights to reduce RO duration.

Execute high-impact, agile exploratory analyses to address critical, time-sensitive business questions by mining and transforming massive, high-dimensional structured and unstructured datasets.

Leverage LLMs and agentic workflows to extract intelligence from data and automate analytical workflows.

Skills

Required

Python
SQL
ML model training
ML model evaluation
ML model fine-tuning
forecasting models
big data manipulation
statistical analysis
optimization
data cleaning
data preprocessing
feature engineering
generative AI environments

Nice to have

Ph.D. in a quantitative field
Google Cloud Platform (GCP)
BigQuery
Vertex AI
Cloud Build
Cloud Run
automotive industry experience
autonomous AI agents
agentic workflows

What the JD emphasized

hands-on experience training, evaluating, and fine-tuning ML, deep learning, and forecasting models

processing and engineering large, high-dimensional structured and unstructured datasets

proficiency in Python and SQL

modern generative AI environments

building LLM-based applications, including autonomous AI agents or agentic workflows

Other signals

Leverage LLMs and agentic workflows to extract intelligence from data and automate analytical workflows.

Partner cross-functionally with data engineers, software engineers, and architects to build robust model pipelines, validate outputs, and deploy scalable ML/LLM models into production GCP environments.

Strong theoretical understanding and practical experience building LLM-based applications, including autonomous AI agents or agentic workflows.

We made history and now we work to transform the future – for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.

Do you believe data tells the real story? We do! Redefining mobility requires quality data, metrics and analytics, as well as insightful interpreters and analysts. That's where Global Data Insight & Analytics makes an impact. We advise leadership on business conditions, customer needs and the competitive landscape. With our support, key decision makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision making.

FCSA (Ford Customer Service Analytics) team, part of Sales and Services Data and Analytics (SSDA) department in Ford’s Global Data, Insight and Analytics (GDI&A) organization, is looking for a highly skilled Data Scientist. This role will be a member of the Repair Order (RO) Duration Analytics team, supporting all stages of RO duration analysis from problem formulation, model/analytical framework development, and evaluation, to model deployment to help Ford achieve RO duration optimization and efficiency objectives.

The RO Duration Analytics team applies advanced quantitative methods, econometrics, and AI/ML/LLM techniques to derive insights and solve a wide variety of challenging problems in various areas of Ford Customer Service Division. This includes: RO duration forecasting, RO duration monitoring/trend study, RO duration root causes, repair process optimization and analytical support, vehicle off-road/uptime, and early quality detection… through analyzing vast amounts of data. This role will be instrumental in supporting business objectives and transformation through data-driven decision-making.

**Model Development & Forecasting: **Design, build, scale, and maintain RO duration forecasting and predictive models, leveraging operational and market inputs such as repair types, fuel types, customer segments, product quality, service parts availability, dealer capacity, and macroeconomic indicators.
Data Quality & Governance: Establish and ensure high standards of data quality, model governance, and validation testing throughout the entire analytics development lifecycle.
Root-Cause & Deep-Dive Analysis: Lead deep-dive and root-cause analyses on vehicles with extended repair times to identify early indicators of anomalies, isolate inefficiencies, and uncover actionable insights to reduce RO duration.
Agile Exploratory Studies: Execute high-impact, agile exploratory analyses to address critical, time-sensitive business questions by mining and transforming massive, high-dimensional structured and unstructured datasets.
GenAI Innovation: Leverage LLMs and agentic workflows to extract intelligence from data and automate analytical workflows.
Model Deployment & MLOps: Partner cross-functionally with data engineers, software engineers, and architects to build robust model pipelines, validate outputs, and deploy scalable ML/LLM models into production GCP environments.
Technical Translation & Communication: Independently translate complex quantitative methodologies and modeling outputs into clear, compelling, and actionable insights for non-technical business partners and executive leadership.

Minimum Qualifications:

Master’s Degree in Data Science, Statistics, Economics, Engineering, Physics, or related quantitative field or a combination of education and equivalent work experience.
3+ years of experience in big data manipulation, statistical analysis, and optimization, including hands-on experience training, evaluating, and fine-tuning ML, deep learning, and forecasting models.
3+ years of experience processing and engineering large, high-dimensional structured and unstructured datasets (including data cleaning, preprocessing, and feature engineering).
3+ years of proficiency in Python and SQL (writing clean, scalable code) along with experience leveraging modern generative AI environments (e.g., OpenCode) to accelerate development and ensure quality.
Strong problem formulation and analytical skills, with a proven ability to self-start and navigate ambiguity in environments where problems are not always well-defined.

Preferred Qualifications:

Ph.D. in a quantitative field such as Data Science, Statistics, Economics, Engineering, Physics, or a closely related field.
3+ years of experience with Google Cloud Platform (GCP), specifically BigQuery, Vertex AI, Cloud Build, and Cloud Run.
3+ years of postgraduate work experience in the automotive industry involving complex quantitative analytics and modeling.
Strong theoretical understanding and practical experience building LLM-based applications, including autonomous AI agents or agentic workflows.

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 childcare 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 7-8 and ranges from $99,600-$192,900.

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 available for this position.

Domestic relocation 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-PW1

**Model Development & Forecasting: **Design, build, scale, and maintain RO duration forecasting and predictive models, leveraging operational and market inputs such as repair types, fuel types, customer segments, product quality, service parts availability, dealer capacity, and macroeconomic indicators.
Data Quality & Governance: Establish and ensure high standards of data quality, model governance, and validation testing throughout the entire analytics development lifecycle.
Root-Cause & Deep-Dive Analysis: Lead deep-dive and root-cause analyses on vehicles with extended repair times to identify early indicators of anomalies, isolate inefficiencies, and uncover actionable insights to reduce RO duration.
Agile Exploratory Studies: Execute high-impact, agile exploratory analyses to address critical, time-sensitive business questions by mining and transforming massive, high-dimensional structured and unstructured datasets.
GenAI Innovation: Leverage LLMs and agentic workflows to extract intelligence from data and automate analytical workflows.
Model Deployment & MLOps: Partner cross-functionally with data engineers, software engineers, and architects to build robust model pipelines, validate outputs, and deploy scalable ML/LLM models into production GCP environments.
Technical Translation & Communication: Independently translate complex quantitative methodologies and modeling outputs into clear, compelling, and actionable insights for non-technical business partners and executive leadership.