Data & Insights Analyst

Wednesbury

7 months ago

Applications closed

Related Jobs

View all jobs

Insights & Data Analyst: Turn Social Trends into Pitches

Senior Data Analyst & Insights Leader in AI/Data

Lead Data Analyst & Insights Strategist

Junior Data Analyst

Data Engineer | Wolves FC

Pricing & Insights Data Analyst (Hybrid UK/France)

DATA & INSIGHTS ANALYST (Power BI)
Wednesbury based - Fully on site role (5 days per week)
£40K - £50K based on experience

Working for an ambitious organisation in the manufacturing industry, this brand new role will be play a key part in the transformation of the business. As part of a strategic shift towards a more data-driven approach, the company have invested in new systems and processes to enhance efficiency, improve decision-making, and drive business performance.

The Data & Insights Analyst will play a crucial role in supporting business operations with deep data insights. This position requires a strong technical skillset and the ability to translate complex data into actionable business recommendations. The Data & Insights Analyst will work cross-functionally across procurement, inventory management, logistics, sales, and finance, optimising decision-making through advanced data analysis and reporting. Experience working in a finance function or any finance understanding would be ideal.

Responsibilities Included:

Analyse and interpret operational and financial data to support decision-making across procurement, inventory, logistics, and sales.
Develop and maintain interactive dashboards and reports (Excel, Power BI, SQL, Business Central) to provide insights into business performance and key trends.
Identify patterns in large datasets, delivering actionable recommendations to optimise processes.
Ensure data integrity, consistency, and compliance with internal policies and regulations.
Assist in forecasting, budgeting, and business planning by providing accurate and timely data insights.In this role you will join a small IT / Finance team and be the sole data person so must be comfortable with this

Skills needed:

Proven experience in a data analyst role, ideally within supply chain, wholesale, and B2B environments.
Strong proficiency in Excel (PivotTables, VLOOKUP, data modelling), Power BI, and SQL.
Experience working with Business Central systems (or similar).
Excellent analytical and problem-solving skills, with the ability to interpret complex datasets.
Strong communication skills, able to explain insights to non-technical stakeholders.Data & Insights Analyst - GleeIT

At Gleeson Recruitment Group, we embrace inclusivity and welcome applicants of all backgrounds, experiences, and abilities. We are proud to be a disability confident employer.

By applying you will be registered as a candidate with Gleeson Recruitment Limited. Our Privacy Policy is available on our website and explains how we will use your data

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.

Jan 18, 2026

Careers Jobs

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

Jan 13, 2026

Jobs

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Jan 6, 2026

Education Jobs

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.