Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Analyst Apprenticeship

Coventry
2 days ago
Create job alert

Path2 Solutions have fantastic opportunity for Data Analyst Apprentices to start their career journey with a leading regional employer in Coventry. The company will provide an environment which will allow you to flourish and develop skills to succeed long term.
 
Successful candidates will be a valued member of our team and daily responsibilities will include observing senior data analysts and learning new skills relevant with the job roles, collecting and analysing important and sensitive data looking for trends that will benefit the business, use Microsoft office and excel to capture data and working as part of a team to achieve collective goals.
 
Apprentice Data Analysist Benefits:

Chance to make a career in a leading business
Strong focus on career progression
Sharesave scheme
Industry leading pension scheme
Bonus scheme
Dedicated training and development academy
Pay rate: £12.21 per hour

37.5 hours per week flexible to suit operational requirements

Related Jobs

View all jobs

Data Analyst Apprentice

Level 4 Data Analyst

Data Science Apprentice

Data Analyst Skills Coach Associate

Data Analyst Skills Coach Associate

HR Systems Data Analyst Level 6 Degree Apprenticeship 2026

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.