Operations Analyst

Consult Energy UK
Leeds
11 months ago
Applications closed

Related Jobs

View all jobs

Healthcare Operations Data Analyst — Junior

Data Analyst - Stock & Operations Insights (Manchester)

Data Analyst - Workforce & Planning Analytics

Data Analyst

Data Analyst

Data Analyst

Operations Analyst


Consult Energyare working with a supplier who is committed to supplying businesses with 100% clean, UK-generated energy. They are seeking an experiencedOperations Analystwho has strong knowledge inHalf Hourly Meteringto join the business. As an Operations Analyst, you will play a key role in optimising operational processes and ensuring the accuracy and integrity of data management for Half Hourly (HH) and NHH metering and assisting with COT’s, data flows and validating trades.


Key Responsibilities – Operations Analyst

  • Support maintenance of excellent settlement performance across both HH and NHH
  • Ensure accurate processing, validation, and submission of HH data to meet industry standards and regulatory requirements.
  • Collaborate with third-party vendors, to resolve any data discrepancies or issues.
  • Identify and implement improvements in processes to drive efficiency
  • Produce reports and analysis related to HH data, helping guide operational decision-making.
  • Support with wider operational supply tasks such as processing change of tenancies, metering siteworks and dealing with customer queries


Key Skills and Experience – Operations Analyst

  • Proven experience in a similar operations or data analyst role within the UK energy market is essential.
  • In-depth knowledge ofHalf Hourly Meteringprocesses, data flows, and systems, minimum of 3 years HH experience is essential.
  • Strong analytical skills with experience in data analysis, reporting. Advanced user of Excel or Google Sheets. SQL experience is desirable but not essential
  • Ability to communicate clearly with internal and external stakeholders.
  • A proactive, detail-oriented, and problem-solving mindset.
  • Fun and energetic approach to the working day.


Location– Fully remote with occasional travel to office (London)


Salary -£40k to £50k dependent on experience


Benefits

25 days annual leave plus bank holidays

Matched pension scheme

Buy and Sell holidays

Private Medical insurance


Operations Analyst

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

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.