Manufacturing Data Scientist

Randstad Inhouse Services

Halewood

1 month ago

Applications closed

Related Jobs

View all jobs

Junior Data Scientist / Data Analyst

Data Scientist

Data Analyst / Junior Data Scientist

Data Analyst/Junior Data Scientist

Data Scientist & Engineer - London

Data Engineer

Manufacturing Data Scientist

Salary: £46,587.88 (inclusive of 35% holiday bonus for 33 days per year; 25 vacation & 8 bank holidays)

Contract: Permanent

Hours: Monday to Thursday: 07:00 - 15:30, Friday: 07:00 - 12:30

As a Manufacturing Data Scientist, you will play a key role in shaping how data is used to improve efficiency, quality, throughput, and sustainability across the plant.

You will design, develop, and maintain a portfolio of data-driven products and projects that turn complex manufacturing data into clear, actionable insights for operators, engineers, and leadership. You will work as part of the plant manufacturing team while also being embedded within Ford's wider global data science and analytics community, helping to scale successful solutions across the enterprise.

This role embodies Ford's commitment to continuous improvement and data-led decision-making, enabling teams to adapt and improve based on the insights you deliver.

Essential

Degree-level education in a relevant subject (such as Mathematics, Statistics, Data Analytics, Computer Science, Physical Sciences) or equivalent professional experience within an engineering or automotive environment
Strong Python expertise
Experience applying machine learning techniques in real-world scenarios
Solid grounding in statistical methodologies and analysis

Desirable

SQL proficiency
Experience with cloud computing platforms

What You'll Do

Leadership & Ford+ Behaviours

Demonstrate Ford+ behaviours in your daily work: ownership, collaboration, integrity, inclusion, customer focus, and continuous learning
Lead or co-lead cross-site analytics initiatives and contribute to a shared analytics playbook

Data, Analytics & Insight

Extract, transform, analyse, and report manufacturing data from multiple sources
Put robust data quality, governance, and security controls in place
Identify process bottlenecks and key drivers of variability to improve OEE, yield, scrap, downtime, cycle times, and energy usage
Build clear dashboards and visualisations, communicating insights in accessible, non-technical language

Modelling & Deployment

Develop and deploy predictive and prescriptive models (e.g. predictive maintenance, defect forecasting, anomaly detection, capacity planning)
Operationalise models using cloud and MLOps best practices, including monitoring, documentation, retraining, and explainability

Collaboration & Change

Work closely with engineering, quality, maintenance, IT, production, and supply chain teams to translate insights into action
Support pilot projects and help scale successful solutions across sites
Contribute to analytics training and capability-building within the plant

Ethics, Safety & Governance

Ensure data privacy, security, and compliance considerations are embedded in all analytics work
Champion responsible, safe, and ethical use of data and models

Benefits

Access to our Employee Development and Assistance Programme
A unique opportunity to access Fords Privilege scheme - allowing you to purchase Ford vehicles at a discount
A great salary increasing yearly, along with our competitive pension scheme
An excellent work-life balance, including a generous holiday allowance of 25 days (inclusive of set shutdown dates)
Cycle to Work Scheme
On site facilities such as a gym, sauna and steam room

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 10, 2026

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Jan 23, 2026

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.