Data Science Supervisor

We are seeking a hands-on Data Science Supervisor to lead a team of software developers and data scientists building diagnostic and quality analytics products for Ford vehicles. In this role, you will guide the team in turning diagnostic trouble codes (DTCs), Data Identifiers (DIDs), and Controller Area Network (CAN) signal data into actionable insights that help identify and resolve vehicle quality issues.

This role requires a combination of AI/ML engineering expertise, people leadership, product delivery, and stakeholder partnership. You will guide work across the product lifecycle-from data ingestion and model development to deployment of interactive dashboards backed by scalable Google Cloud Platform (GCP) infrastructure. You will champion agile practices, mentor team members, and partner with product, quality, and engineering stakeholders to deliver useful, reliable analytics products.

Lead a cross-functional team of software developers and data scientists to design and deliver cutting-edge automotive analytics products. Leverage diagnostic trouble codes (DTCs), Data Identifiers (DIDs), and CAN signal data, combined with AI-driven anomaly detection, to surface vehicle quality issues and power intuitive Angular dashboards on Google Cloud Platform. Please note: This is a mandatory Transitional Work Arrangement (TWA) position.

Data Science, AI & Product Strategy

• Guide the design and development of anomaly-detection models using DTCs, DIDs, and CAN signal data to identify emerging vehicle quality issues.
• Help shape the team's analytics product roadmap by connecting stakeholder needs, vehicle quality priorities, data insights, and technical opportunities.
• Evaluate and integrate Generative AI, retrieval-augmented generation (RAG), natural language processing (NLP), and other emerging capabilities where they can add value.
• Drive model validation, performance monitoring, and continuous improvement to support production-grade accuracy and reliability.
• Translate vehicle quality and diagnostic challenges into practical data science and software solutions.

Platform & Product Delivery

• Oversee the design, development, and deployment of Angular-based dashboards that present diagnostic and quality insights to internal stakeholders.
• Guide the development of scalable, end-to-end ML pipelines on GCP, including data ingestion, processing, modeling, deployment, and monitoring.
• Partner with technical teams to ensure products are reliable, maintainable, secure, and fit for operational use.
• Manage team priorities, resources, and delivery timelines in support of the product roadmap.
• Identify and address delivery risks, technical dependencies, and opportunities to improve product performance.

Team Leadership & Organizational Effectiveness

• Lead, coach, and develop a team of software developers and data scientists delivering diagnostics and quality analytics products.
• Establish clear team priorities, roles, expectations, and accountability.
• Foster a collaborative, inclusive, and high-performing team environment focused on customer value, quality, and continuous improvement.
• Support workforce planning, knowledge sharing, technical development, and career growth.
• Promote disciplined execution and strong collaboration across software engineering and data science workstreams.

Engineering Practices & Continuous Improvement

• Champion agile practices, including sprint planning, backlog refinement, and retrospectives.
• Establish and reinforce engineering practices such as code review, testing, CI/CD, and version control across data science and AI work.
• Support the use of MLOps practices for model deployment, monitoring, and lifecycle management.
• Identify opportunities to improve data pipelines, model workflows, software delivery, and operational practices.
• Stay current on advances in automotive diagnostics, AI/ML, and cloud technologies, and assess their potential application to team products.

Stakeholder Engagement & Communication

• Partner with quality, engineering, and product teams to understand business and vehicle quality needs and define effective solutions.
• Communicate technical findings, model performance, product roadmaps, risks, and delivery progress to technical and non-technical stakeholders.
• Build trusted working relationships across the organization and help align stakeholders on priorities, decisions, and outcomes.

Data Science, AI & Product Strategy

• Guide the design and development of anomaly-detection models using DTCs, DIDs, and CAN signal data to identify emerging vehicle quality issues.
• Help shape the team's analytics product roadmap by connecting stakeholder needs, vehicle quality priorities, data insights, and technical opportunities.
• Evaluate and integrate Generative AI, retrieval-augmented generation (RAG), natural language processing (NLP), and other emerging capabilities where they can add value.
• Drive model validation, performance monitoring, and continuous improvement to support production-grade accuracy and reliability.
• Translate vehicle quality and diagnostic challenges into practical data science and software solutions.

Platform & Product Delivery

• Oversee the design, development, and deployment of Angular-based dashboards that present diagnostic and quality insights to internal stakeholders.
• Guide the development of scalable, end-to-end ML pipelines on GCP, including data ingestion, processing, modeling, deployment, and monitoring.
• Partner with technical teams to ensure products are reliable, maintainable, secure, and fit for operational use.
• Manage team priorities, resources, and delivery timelines in support of the product roadmap.
• Identify and address delivery risks, technical dependencies, and opportunities to improve product performance.

Team Leadership & Organizational Effectiveness

• Lead, coach, and develop a team of software developers and data scientists delivering diagnostics and quality analytics products.
• Establish clear team priorities, roles, expectations, and accountability.
• Foster a collaborative, inclusive, and high-performing team environment focused on customer value, quality, and continuous improvement.
• Support workforce planning, knowledge sharing, technical development, and career growth.
• Promote disciplined execution and strong collaboration across software engineering and data science workstreams.

Engineering Practices & Continuous Improvement

• Champion agile practices, including sprint planning, backlog refinement, and retrospectives.
• Establish and reinforce engineering practices such as code review, testing, CI/CD, and version control across data science and AI work.
• Support the use of MLOps practices for model deployment, monitoring, and lifecycle management.
• Identify opportunities to improve data pipelines, model workflows, software delivery, and operational practices.
• Stay current on advances in automotive diagnostics, AI/ML, and cloud technologies, and assess their potential application to team products.

Stakeholder Engagement & Communication

• Partner with quality, engineering, and product teams to understand business and vehicle quality needs and define effective solutions.
• Communicate technical findings, model performance, product roadmaps, risks, and delivery progress to technical and non-technical stakeholders.
• Build trusted working relationships across the organization and help align stakeholders on priorities, decisions, and outcomes.