Manufacturing Data Scientist

Location: Knowsley

Job title:

Manufacturing Data Scientist

Reference: 50899

Location:

Halewood, Merseyside

Duration:
Permanent

Start date:

ASAP

Salary: £46,587.88 pa + 33 days holiday per year; 25 vacation & 8 bank holidays

GPW Recruitment are partnering with Ford Halewood Transmissions Ltd (FHTL) in Halewood to recruit a Manufacturing Data Scientist.

Ford Halewood Transmission Limited (FHTL) develops and manufactures transmissions with an employee workforce of circa 600 people. The Plant has a proud 60-year history as a local employer and are dedicated to manufacturing high quality products. Ford are currently investing up to £230 million at the facility to transform it to build electric power units for future Ford all-electric passenger and commercial vehicles.

On Offer as the Manufacturing Data Scientist

* Salary of £46,587.88 pa

* 33 days holidays per year(25 vacation & 8 bank holidays)

* Hours of work are Monday to Thursday 7:00-15:30 and Friday 7:00-12:30

Plus FHTL Employment Benefits:

* Free on-site gym facility inclusive of a sauna & steam room (outside of working hours).

* Employee assistance programmes: weekly appointments available for all employees to utilise for free such as massages, circuit classes, nutrition advise, yoga, chiropody, reiki and head massages (outside of working hours).

* An on-site physiotherapy and occupational health department is available to employees to support their health and well-being.

* Competitive pension scheme (company pays 1.5 times the amount of the employee contributions paying up to 12%)

 * £750 annual attendance bonus (subject to company T&C’s).

* Access Ford's Privilege scheme - allowing you to purchase Ford vehicles at a discount

* An excellent work-life balance, including a generous holiday allowance of 25 days (inclusive of set shutdown dates)

* Cycle to Work Scheme

The role

A data science leader within Ford’s manufacturing ecosystem who conceptualises, develops, and maintains a portfolio of data-driven products and projects  role delivers measurable improvements in plant efficiency, quality, and throughput by turning complex data into actionable insights for operators, engineers, and leadership.

You will contribute to a growing digital/data science community within the plant, collaborate across Ford’s central analytics network, and help scale successful solutions enterprise-wide.

Ford Motor Company’s ethos is to compete to win, to enable this, a key priority is deriving meaningful insights from data. At Ford Halewood Transmissions Ltd, manufacturing data is the priority, this role embodies continuous improvement, enabling teams to adapt to the insights that you provide. You will be part of the plant manufacturing team and part of the wider data science pillar in Ford.

You will be responsible for conceptualising, developing and maintaining a range of manufacturing data science projects and products.

Qualifications and Experience required by the Manufacturing Data Scientist

Essential:

Degree level education in relevant subject (Mathematics, Statistics, Data Analytics, Computer Science a Physical Science or related) or equivalent experience relevant to the field in an engineering/automotive environment.

Essential:

Python expertise

Essential:

Expertise using ML techniques

Essential:

Experience using statistical methodologies

Preferable:

SQL proficiency

Preferable:

Cloud computing proficiency

Key Responsibilities (including but not limited to):

Ford+ Behaviours and leadership

* Demonstrate Ford+ Behaviours in daily work: ownership, collaboration, customer focus, integrity, inclusion, and learning. Role-model these behaviours in cross-functional projects and in mentoring others.

* Lead or co-lead cross-site analytics initiatives, sharing best practices and building a plant-level analytics playbook.

Data collection, transformation, and governance

* Extract, transform, load, analyse, and report complex manufacturing data from multiple sources.

* Establish and maintain data quality checks, metadata, lineage, governance, and security controls to ensure trustworthy analytics.

Insight generation and storytelling

* Identify bottlenecks and variability drivers to improve OEE, yield, scrap reduction, downtime, cycle…