Product Support Engineer

Coventry
8 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Engineer

Senior Data Engineer - Azure

Data Engineer

Data Engineer - Amplitude/ Mixpanel Exp

Data Engineer

Expleo is seeking a proactive and detail-oriented Product Support Engineer, to support one of our high-profile clients in the delivery and optimisation of build operations across their programmes. This role plays a crucial part in driving operational readiness, logistics, and part management in a fast-paced manufacturing and engineering environment.

You'll be central to the coordination of resource and capacity planning, logistics for unit movements, and delivery of continuous improvement projects within the build facility. With varied responsibilities and multiple stakeholders, this role suits someone with excellent multitasking, stakeholder engagement, and process optimisation skills.

Key Responsibilities

Manage shortage parts post-allocation, ensuring correct movement between stores and build zones.
Oversee the missing, damaged, and incorrect part processes within build operations.
Coordinate rework activities, ensuring proper documentation and materials withdrawal processes.
Collaborate with stakeholders across Stores, NRP, Supply Chain, Build Planning, and Operations to resolve part kit issues and ensure completeness.
Investigate root causes of part pick issues using structured problem-solving methodologies.
Create and manage pick kit matrices for each build using Wrike or similar platforms.
Lead systems-based investigations for part queries and shortages using tools such as GPIRS and SAP (or equivalents).
Manage returned parts and reintegrate them into the build process where applicable.
Act as the primary escalation point for shortage risks affecting build operations.
Generate and maintain operational reports and dashboards to support data-driven decision-making.
Support continuous improvement initiatives across logistics and build preparation functions.
Respond to additional requests from area management or leads as required.Essential Skills & Experience

Strong IT competency: Intermediate to advanced Excel skills (VBA, Power Query, pivot tables, formulas).
Experience with project/task management tools such as Wrike or Microsoft Project.
Understanding of GPIRS, SAP, or similar Bill of Materials and parts-tracking systems.
Confident communicator with strong organisational and multitasking abilities.
Full UK driving license required for site travel. Desirable Skills

Prior experience in a logistics or parts coordination role, ideally in a manufacturing or engineering environment.
Proficiency in data analysis and progrem-solving, with a methodical, root cause-driven approach.
Familiarity with Business Intelligence tools (e.g., Power BI, Tableau) to create and maintain dashboards.
Stakeholder management experience, with the ability to influence and collaborate across teams.Education & Technical Background

Degree or vocational training in Data Analytics, Production/Manufacturing Engineering, Logistics, or Business Administration.
Proven experience in handling complex data, ideally involving cross-platform data analysis.đź“© Interested? Apply now or get in touch to find out more about this opportunity with Expleo.

Leanne Eaton

(url removed)

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 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.

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.

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.

🍪 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.