Inventory Data Analyst

Manpower
Bridgwater
11 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst - Quality Control

Data Analyst - Supply Chain

Head of Data Science

Data Science Consultant - Remote

Data Science Consultant - Remote

Data Scientist - Product Operations

Role -Inventory Data Analyst

Location -Bridgwater

Pay rate -£145 per day

Hours -Full- time 37 hours per week, working Monday - Friday.

A career that will deliver change.

The Opportunity

We are seeking an experienced and competent Catalogue Inventory Analyst to join a growing asset data management team. The individual will ideally come with extensive previous cataloguing and analytical experience, data entry and validation related knowledge and skills, and rapidly grow to become a key member of a small but dedicated Enterprise Asset Management (EAM) data management team.

The Role

Accurate and timely population of the Inventory module of the Asset Suite 9 EAM Tool with requisite information relating to asset equipment and bulk materials, including unique asset identifiers and the associated set of pre-defined asset data attributes, to support their timely and accurate call off from the warehouse to HPC site. Complete the quality assurance of all Catalogued assets as a key enabler to both their fully auditable call off, installation and management of the plant's configuration reference. Maintain a predefined asset cataloguing schedule, working closely with the line manager to proactively identify and manage any data verification issues and anomalies, so maintaining responsibility for data integrity and load schedules Producing weekly cataloguing performance reports into the line manager for review, approval and upward reporting. Work independently to achieve targets but in conjunction within the existing AS9 Work Management Team(WMT) to support broader team targets and the delivery of consistent and verified data and/or documented references across a number of integrated IS platforms.

The Skills

The ideal candidate will possess proven asset cataloguing, data analysis, verification and entry skills and a capacity to logically process data (and documentation), whilst both identifying data inconsistencies and managing their resolution independently, or where required, with a senior member of the team. The successful candidate will possess specific prerequisite skills and qualifications including:

A solid track record of digital cataloguing and assured data entry, and/or using SAP and/or EDRM databases. Previous experience of working in a construction, supply chain, procurement and/or data management related industry. A proven track record of adept and precise data and documentation management skills. A proven ability to work without supervision is essential. Proficient use of Microsoft products including MS: Excel; Word,and Powerpoint. Knowledge of Asset Suite/Passport will be advantageous but is not essential.

null

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.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.