Utilities Data Analyst

Red Door Recruitment
St Albans
8 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Senior Data Engineer

Portfolio Revenue & Debt Data Scientist

Data Architect

We are currently recruiting for an analytical and highly numerate candidate with an excellent attention to detail to join a fast-growing, exciting company based in central St Albans. They operate in modern open plan offices with free parking!
You will lead the resolution of utility invoice discrepancies and ensure the accuracy of utility data across systems.
The role involves working closely with utility suppliers and internal teams to investigate billing issues, maintain strong supplier relationships, and ensure data integrity for accurate reporting and invoice approval.
This role would suit a detail-oriented, tenacious individual with a strong track record of seeing tasks through to resolution.
What’s in it for you?

  • Salary: up to £35-40k
  • Monday to Friday 9-5 – hybrid working 1 day from home
  • 22 days holiday plus 8 days bank holiday
  • Free parking
  • Private medical insurance
  • Life insurance
  • Employee assistance programme
  • Online discount programme
    Key responsibilities:
  • Investigate and resolve issues identified during invoice validation
  • Liaise directly with suppliers to challenge discrepancies and drive them to resolution
  • Ensure consistent follow-through on issues from initiation to closure
  • Retain relationships with dedicated supplier account managers, especially during transitions between suppliers, to avoid reliance on generic call centres
  • Escalate issues appropriately to maintain service quality and continuity
  • Provide regular updates to internal stakeholders
  • Collaborate closely with colleagues
  • Ensure issues are fully resolved before invoice approval.
  • Maintain accurate, clean, and reliable data in to support ongoing validation and reporting
    What the employer is looking for:
  • Experience with analysing data or reconciling complex data
  • Exceptional attention to detail – able to spot and investigate anomalies in data
  • A strong completer-finisher who sees tasks through to final resolution
  • Tenacious, self-starting character – able to work independently and persist through challenges
  • Proficiency in Microsoft Excel – comfortable working with large datasets, formulas, and analysis tools
  • Willingness and ability to learn systems and processes quickly
  • Excellent written and verbal communication skills
    Red Door Recruitment is committed to encouraging equality, diversity and inclusion among our workforce, and eliminating unlawful discrimination. Full details available on our website.
    Please note due to the number of applications we often receive; only shortlisted applicants will be contacted

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.

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.

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.

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.