Operations Analyst - Sport and Football enthusiast essential

Leeds
10 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

A leading Leeds-based tech organisation is looking for a passionate Football and Data Enthusiast to join their team as an Operations Analyst. If you're a football fan with a love for data, this is the perfect opportunity to turn your passion into a career!

Key Responsibilities:

Manage and analyse large volumes of data to ensure accuracy and functionality across various systems.
Monitor systems to ensure data is behaving as it should. If data isn't performing correctly, investigate and resolve the issue.
Map data, ensuring it's organised and flows correctly across different systems.
Constantly look for ways to improve data processes and identify areas for improvement.
Work with spreadsheets, ensuring data is well-managed and accurate.
Collaborate with your team to ensure smooth and efficient operations.
No direct customer contact, but a vital part of the team ensuring everything runs efficiently behind the scenes.

Shifts & Schedule:

5 shifts a week, including weekends.
3 shifts: 9am - 5pm.
2 late-night shifts per month: until 10pm.
Weekend shifts will start at 7am, 9am, or 1pm for 8-hour shifts.

Ideal Candidate:

Data Science Graduate or someone with experience in a data-related role.
A genuine love of football and sport.
Strong ability to manage large sets of data and work with spreadsheets (Excel, Google Sheets, etc.).
An analytical mindset with the ability to identify issues and solve problems when data isn't behaving as expected.
A keen eye for improvement opportunities within data processes.
Excellent attention to detail and strong organisational skills.If you're ready to combine your passion for football with your love for data and work in an exciting tech-driven environment, we'd love to hear from you.

Huntress Search Ltd acts as a Recruitment Agency in relation to all Permanent roles and as a Recruitment Business in relation to all Temporary roles.

We practice a diverse and inclusive recruitment process that ensures equal opportunity for all we work with, irrespective of race, sexual orientation, mental or physical disability, age or gender. As an organisation, we encourage applications from all backgrounds and will ensure measures are met when required, to allow a fair process throughout.

PLEASE NOTE: We can only consider applications from candidates who have the right to work in the UK

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.

Jobs

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.

Education Jobs

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.

Jobs

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.

🍪 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.