Manufacturing Data Scientist

Experience and skills for being effective in the role are:

• Technical Proficiency: Strong proficiency in Python for data analysis, machine learning and statistical modelling (e.g. pandas, NumPy, scikit-learn), alongside familiarity with cloud-based data platforms, preferably Google Cloud.

• Analytical Modelling: Proven experience developing analytical, statistical or machine learning solutions within complex operational environments, coupled with a strong desire to continuously improve model performance, data quality and business outcomes.

• Project Execution: Demonstrated ability to lead analytical projects from problem definition through development, deployment and business adoption.

• Communication & Stakeholder Management: Excellent communication and facilitation skills, with the ability to manage stakeholder expectations, influence decision making and work effectively in complex environments.

• Data Engineering: Hands-on experience building and maintaining scalable data pipelines and analytical data products using structured data engineering practices.

• Problem Solving: Ability to translate complex business problems into structured analytical frameworks, supported by a passion for uncovering insights and solving challenging real-world manufacturing problems.

• Manufacturing Acumen & Domain Knowledge (Preferred): Direct experience working with manufacturing, industrial, IoT, connected equipment or Industry 4.0 data environments. Experience within automotive, battery, propulsion systems or other advanced manufacturing sectors would be advantageous.

You will have:

• Degree in Data Science, Computer Science, Engineering, Mathematics or a related quantitative discipline.

• If you do not hold a degree but possess relevant skills and experience equivalent to this level of education, we would still welcome your application.

Additional Information

• Ability to travel periodically to Ford manufacturing plants and suppliers globally is essential.

• This position is based in Dunton and it is expected the successful candidate will typically attend the office or manufacturing facilities a minimum of four days per week.

• We are committed to creating a diverse and inclusive workplace where everyone can contribute and succeed.

The Company is committed to diversity and equality of opportunity for all and is opposed to any form of less favourable treatment or harassment on the grounds of race, religion or belief, sex, marriage and civil partnership, pregnancy and maternity, age, sexual orientation, gender reassignment or disability

This position is based in Dunton, and it is expected the successful candidate will be able to attend the Dunton Campus for typically 4 days a week and remain flexible on the days they are required to attend the office according to business requirements.

As part of our pre-employment checks process, successful candidates will be required to undergo a criminal record check. This will be conducted in line with the Rehabilitation of Offenders Act 1974 and applied only to unspent convictions.

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Your role will:

• Develop and Maintain: Build machine learning, statistical and forecasting models to optimise manufacturing processes and extract actionable intelligence from equipment, process and operational data.

• Design and Build: Create and manage scalable data pipelines using Python and Google Cloud Platform (GCP) to ingest, transform and prepare complex manufacturing data for analysis and deployment.

• Interface and Translate: Work directly with internal and external stakeholders to translate manufacturing and business challenges into structured analytical frameworks and data-driven solutions.

• Facilitate: Act as a strong facilitator to gather requirements, manage stakeholder expectations, validate analytical outputs and support organisational adoption of analytical solutions.

• Standardise: Perform data reconciliation, cleansing and standardisation across diverse manufacturing systems to ensure high data quality, consistency and analytical integrity.

• Support Launches: Provide technical assistance and data-driven insight during the launch of new manufacturing processes, equipment and programmes.

• Communicate: Present analytical findings and actionable recommendations clearly, adapting communication styles for both technical and non-technical audiences.

• Collaborate: Work closely with innovation, software, data engineering and enterprise data teams to ensure analytical solutions are scalable, deployable and aligned with broader technology strategies.

• Mentor and Document: Teach, coach and mentor peers while documenting technical knowledge, analytical approaches and best practices to support long-term capability growth.

• Drive Continuous Improvement: Foster a culture of continuous learning, collaboration, technical excellence and data-driven decision making.

• On-Site Execution: Travel to Ford manufacturing plants and supplier locations globally, as required by project and programme needs, to understand operational challenges, support implementations and collaborate directly with on-site teams.

Experience and skills for being effective in the role are:

• Technical Proficiency: Strong proficiency in Python for data analysis, machine learning and statistical modelling (e.g. pandas, NumPy, scikit-learn), alongside familiarity with cloud-based data platforms, preferably Google Cloud.

• Analytical Modelling: Proven experience developing analytical, statistical or machine learning solutions within complex operational environments, coupled with a strong desire to continuously improve model performance, data quality and business outcomes.

• Project Execution: Demonstrated ability to lead analytical projects from problem definition through development, deployment and business adoption.

• Communication & Stakeholder Management: Excellent communication and facilitation skills, with the ability to manage stakeholder expectations, influence decision making and work effectively in complex environments.

• Data Engineering: Hands-on experience building and maintaining scalable data pipelines and analytical data products using structured data engineering practices.

• Problem Solving: Ability to translate complex business problems into structured analytical frameworks, supported by a passion for uncovering insights and solving challenging real-world manufacturing problems.

• Manufacturing Acumen & Domain Knowledge (Preferred): Direct experience working with manufacturing, industrial, IoT, connected equipment or Industry 4.0 data environments. Experience within automotive, battery, propulsion systems or other advanced manufacturing sectors would be advantageous.

You will have:

• Degree in Data Science, Computer Science, Engineering, Mathematics or a related quantitative discipline.

• If you do not hold a degree but possess relevant skills and experience equivalent to this level of education, we would still welcome your application.

Additional Information

• Ability to travel periodically to Ford manufacturing plants and suppliers globally is essential.

• This position is based in Dunton and it is expected the successful candidate will typically attend the office or manufacturing facilities a minimum of four days per week.

• We are committed to creating a diverse and inclusive workplace where everyone can contribute and succeed.

The Company is committed to diversity and equality of opportunity for all and is opposed to any form of less favourable treatment or harassment on the grounds of race, religion or belief, sex, marriage and civil partnership, pregnancy and maternity, age, sexual orientation, gender reassignment or disability

This position is based in Dunton, and it is expected the successful candidate will be able to attend the Dunton Campus for typically 4 days a week and remain flexible on the days they are required to attend the office according to business requirements.

As part of our pre-employment checks process, successful candidates will be required to undergo a criminal record check. This will be conducted in line with the Rehabilitation of Offenders Act 1974 and applied only to unspent convictions.

#LI-EO2