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. In this role you will apply data science techniques to analyze complex datasets, develop predictive models, and generate actionable insights to drive business value for Ford.  You will engineer data pipelines, perform statistical analysis, and build machine learning models. You will effectively communicate findings and collaborate with technical and non-technical stakeholders to translate data-driven insights into tangible solutions.  You get to work with a unique blend of engineers, DevOps, automation, controls, manufacturing and robotics specialists – ideating, building and scaling Billion-Dollar ideas for the manufacturing of iconic Ford products.  This position requires an individual who is at the forefront of Data Engineering technologies and believes in bringing the latest and greatest to Ford’s plant floor to build impactful use cases that can be industrialized with the latest technologies.  This is a rare opportunity to put your signature on how Ford manufactures vehicles.

• Design and implement scalable data science solutions that turn manufacturing data into actionable insights across the factory network.
• Develop and deploy predictive models (e.g., quality prediction, anomaly detection, throughput forecasting) directly into production systems.
• Collaborate with software engineers and data engineers to build data-centric applications that connect plant-floor data (e.g., PLCs, sensors, MES) with cloud-based analytics.
• Architect and implement components within the guardrails of Ford’s Data-Centric Architecture (DCA), ensuring data pipelines, features, and models are reusable, observable, and aligned to product needs.
• Translate domain and business problems into mathematical and statistical formulations using appropriate modeling techniques.
• Work closely with plant engineers and cross-functional stakeholders to validate, interpret, and continuously improve data-driven systems.
• Ensure robust data quality, governance, and lineage across systems spanning on-prem (factory) and cloud environments.
• Contribute to MLOps practices: versioning, monitoring, retraining, and automated deployment of ML models at scale.

Minimum Qualifications 

• Bachelor’s or master’s degree in data science, Computer Science, Industrial Engineering, Statistics, or a related technical field.
• 3+ years of experience developing data science solutions for operational or industrial use cases.
• Strong programming skills in Python and experience with relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn).
• Strong understanding and experience with data engineering fundamentals—data wrangling, pipeline orchestration, and ETL processes.
• Solid understanding of statistical modeling and machine learning algorithms.
• Experience deploying ML models to production using APIs or embedded in edge/cloud applications.
• Proficiency with relational and distributed databases (e.g., SQL, Spark, Delta Lake) and query languages and experience working with large datasets.
• Experience with cloud platforms (e.g., GCP, AWS, Azure) and their data science services.
• Understanding of version control, testing, and CI/CD in a data science context.
• Strong communication skills and ability to explain technical solutions to cross-disciplinary audiences.
• Strong analytical and problem-solving skills and excellent data visualization skills.

Preferred Qualifications 

• Experience working with manufacturing, industrial IoT, or process control systems.
• Knowledge of Data-Centric Architecture principles and experience industrializing reusable data products (features, labels, models).
• Familiarity with time series forecasting, anomaly detection, root cause analysis, or reinforcement learning in industrial settings.
• Experience with cloud-native data services (e.g., Azure Data Factory, AWS SageMaker, Databricks, or GCP Vertex AI).
• Exposure to edge computing and deploying models close to the source (e.g., plant floor or local gateways).
• Demonstrated experience building pipelines and model management systems that are robust, maintainable, and scalable.
• Experience with MLOps practices and tools.
• Knowledge of experimental design and causal inference.
• Domain knowledge in the automotive industry.