Manufacturing Data Scientist

Randstad Inhouse Services
Halewood
3 weeks ago
Applications closed

Related Jobs

View all jobs

Manufacturing Data Scientist

Data Scientist

Data Scientist

Principal, AI Data Scientist

SAP Master Data Analyst

Data Analyst

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.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

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

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.