Short Description The Global Data Insights & Analytics (GDI&A;) organisation enables Ford to make evidence-based decisions through data, advanced analytics, Artificial Intelligence (AI), and Machine Learning (ML). Within GDI&A;, the Industrial System Analytics (ISA) team partners with Manufacturing, Quality, Supply Chain, Purchasing, Program Management, and Product Development teams to solve complex business challenges and drive measurable business value.

We are seeking an experienced Data Science and Analytics Leader who combines strong analytical expertise with product thinking, stakeholder engagement, and delivery leadership. This role will lead the development of new analytics and AI solutions from concept through deployment by collaborating with business stakeholders, data scientists, software engineers, data engineers, and product teams.

Description

As a Manager within Industrial System Analytics (ISA), you will lead the creation of next-generation analytics and AI solutions that help optimise Ford's manufacturing operations, supply chain, quality, cost, and product development processes.

This role is ideally suited for someone who has a strong foundation in Data Science and Machine Learning and has successfully partnered with business leaders to identify opportunities, translate business problems into analytical solutions, and deliver products that generate measurable business outcomes.

You will play a key role in shaping current products from the ground up, including defining requirements, evaluating data ecosystems, influencing technical direction, building cross-functional teams, and driving successful adoption across global business functions.

Success in this role requires strong communication skills, the ability to influence senior stakeholders, and experience leading multidisciplinary teams in complex enterprise environments.

Responsibilities

Business & Product Leadership

- Partner with business stakeholders across Manufacturing, Supply Chain, Quality, Purchasing, and Product Development to identify business opportunities and define analytics roadmaps.
- Translate ambiguous business challenges into clearly defined analytical and AI-driven solutions.
- Lead the development of new analytics products and capabilities from concept through deployment.




- Drive prioritisation decisions based on business value, feasibility, data availability, and strategic objectives.
- Establish strong relationships with stakeholders across global regions, particularly North America.

Data Science & AI Leadership

- Provide technical leadership across Data Science, Machine Learning, Predictive Analytics, and Generative AI initiatives.
- Guide teams in selecting appropriate analytical approaches, machine learning models, and AI technologies.
- Ensure solutions are built using sound analytical methodologies and aligned with business outcomes.
- Drive adoption of emerging AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and Generative AI solutions where appropriate.
- Review and challenge modelling approaches and technical designs while empowering teams to execute effectively.

Data Strategy & Analytics Delivery

- Assess and understand complex data ecosystems spanning multiple data sources and business processes.
- Work with Data Engineering teams to define data requirements, integration strategies, and data quality improvements.
- Support the development of scalable analytics platforms and data products.
- Enable the transformation of fragmented or manual data sources into enterprise-grade analytical solutions.

Cross-functional Team Leadership

- Lead and mentor multidisciplinary teams including Data Scientists, Data Engineers, Software Engineers, Product Designers, and Delivery Managers.
- Foster collaboration between business, analytics, engineering, and product teams.
- Drive execution through Agile delivery practices and modern software development approaches.
- Build high-performing teams focused on innovation, ownership, and continuous learning.

Delivery & Solution Ownership

- Oversee end-to-end delivery of analytics and AI solutions.
- Collaborate with software engineering teams to ensure analytical capabilities are successfully operationalised.




- Ensure products deliver measurable business value and sustainable adoption.
- Monitor outcomes and continuously improve products based on stakeholder feedback and business needs.

Qualifications

Required

- Master's degree in Data Science, Computer Science, Statistics, Engineering, Industrial Engineering, Operations Research, Analytics, or a related field.
- Proven experience leading Data Science, Analytics, AI, or Machine Learning initiatives in enterprise environments.
- Strong experience working directly with business stakeholders to identify opportunities and translate business needs into analytical solutions.
- Demonstrated ability to lead cross-functional teams consisting of Data Scientists, Data Engineers, Software Engineers, Product Managers, or Product Designers.
- Experience delivering analytics solutions from concept through implementation.
- Strong understanding of Machine Learning, Predictive Analytics, Statistical Modelling, and AI solution development.
- Practical experience with Python and modern Machine Learning frameworks such as Scikit-Learn, TensorFlow, PyTorch, or XGBoost.
- Understanding of data engineering concepts, including data integration, data pipelines, and large-scale data environments.
- Experience collaborating with software engineering teams to deliver production-ready solutions.
- Strong communication, presentation, and stakeholder management skills.
- Experience working in global organisations with business stakeholders across multiple regions.
- Familiarity with Generative AI, Large Language Models, Retrieval-Augmented Generation (RAG), and emerging AI technologies.

Preferred

- Automotive, Manufacturing, Supply Chain, Quality, or Industrial Analytics experience.
- Experience building analytics or AI products from the ground up in greenfield environments.
- Experience with Google Cloud Platform (GCP), Spark, SQL, Hive, or contemporary cloud analytics platforms.
- Exposure to Product Management principles, roadmap development, and product strategy.
- Experience leading geographically distributed teams.
- Understanding of software development lifecycle, Agile delivery methodologies, and enterprise architecture practices.
- Knowledge of MLOps, AI governance, and model deployment practices.

📌 Manager, Data Science & Product Leadership (Chennai)
🏢 Ford Business Solutions
📍 Chennai