Data Science Engineering Manager

**ML & Data Science Engineering Manager**

**In this role**

This is a great opportunity to drive the delivery of a key enterprise objective in building Ford’s flagship products - bring Innovation in Manufacturing to have significant business impact.
You will spearhead Ford’s transformation of manufacturing by building the intelligent backbone that will power our factories of the future. As the Engineering Manager for Data Science, you will establish and lead a new, specialized team in Mexico dedicated to creating AI-native infrastructure and processes within our Manufacturing IT ecosystem. Your mission is to harness the power of predictive modeling, machine learning, and real-time data to solve complex manufacturing challenges, drive efficiency, and ensure the quality of our iconic vehicles.

This is a rare opportunity to lead a team that will put its signature on the future of how Ford manufactures vehicles.

**About Ford Manufacturing + Technology**

Ford is transforming how vehicles are built—from traditional assembly lines to digitally connected, intelligent manufacturing systems. At the core of this transformation is software: enabling smarter decisions, predictive maintenance, real-time process optimization, and seamless data flow between machines, people, and the cloud.

**You'll Have**
- A Bachelor’s degree in Computer Science, Statistics, Engineering, or a related quantitative field.
- 3+ years of formal leadership experience, managing and mentoring technical teams in a data science or software engineering capacity.
- 5+ years of hands-on experience in data science and machine learning, with deep expertise in predictive modeling, classification, and unsupervised learning techniques (like anomaly detection).
- Proven experience building and deploying machine learning models into production environments.
- Strong proficiency in Python and common data science/ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch, Pandas).
- Experience with cloud computing platforms (e.g., GCP, Azure, or AWS) and their associated AI/ML services.
- Excellent problem-solving skills and the ability to navigate complex, ambiguous challenges.
- Fluency in both English and Spanish, with strong verbal and written communication skills.

**Even better if you have**
- A Master’s degree or PhD in a relevant technical field.
- Experience in a manufacturing, industrial automation, or IoT environment.
- Deep knowledge of MLOps principles and experience with related tools (e.g., Kubeflow, MLflow, Seldon Core).
- Experience with real-time data streaming technologies (e.g., Kafka, Flink) and time-series analysis.
- Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes).A strong portfolio of deployed AI/ML projects that have delivered measurable business value.

Ford Motor Company is an Equal Opportunity Employer, as we are committed with a diverse workforce, and do not discriminate against any employee or applicant
for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran and basis of disability.

**What you’ll do**
- Lead and Mentor: Recruit, hire, and develop a high-performing team of AI/ML and Data Science Engineers. Foster a culture of technical excellence, innovation, and continuous learning.
- Define Technical Strategy: Architect and drive the roadmap for building AI-native infrastructure, platforms, and processes tailored for the manufacturing environment.
- Oversee Model Development & Deployment: Guide the end-to-end lifecycle of machine learning projects—from data acquisition and feature engineering to model training, validation, and deployment of predictive models and anomaly detection systems in production.
- Champion MLOps: Implement and evangelize best practices for MLOps to ensure the scalability, reliability, and continuous improvement of our machine learning systems.
- Drive Business Impact: Collaborate closely with manufacturing operations leaders, plant floor engineers, and IT partners to identify high-value use cases and translate business needs into tangible AI-driven solutions.
- Ensure Technical Excellence: Set and maintain high standards for code quality, system performance, and scientific rigor across all data science and machine learning projects.
- Manage Execution: Lead the team using agile methodologies to deliver projects on time, ensuring clear communication with all stakeholders and managing priorities effectively in a dynamic environment.