Maths Teacher

Barking
9 months ago
Applications closed

Related Jobs

View all jobs

Data Analytics Consultant

Junior / Graduate Data Scientist

Data Analyst Level 4 Apprentice

Data Analyst Level 4 Apprentice

Strategy Data Analyst Level 4 Apprentice

Environmental Data Analyst Level 4 Apprentice

Ignite a Passion for Problem-Solving! Full-Time Maths Teacher at Outstanding School in London (September 2025 Start - KS3, KS4 & KS5)

Are you an inspirational and dynamic Mathematics teacher passionate about making numbers and complex concepts accessible and exciting for every student? Our Outstanding school in London is seeking an enthusiastic Full-Time Teacher of Mathematics to join our vibrant and successful department from September 2025. This is an exciting opportunity to shape the mathematical understanding and confidence of students across Key Stage 3, Key Stage 4 (GCSE), and Key Stage 5 (A-Level) within a diverse and ambitious educational environment.

In this rewarding full-time role, you'll have the opportunity to:

  • Cultivate Numerical Fluency & Reasoning: Deliver engaging and effective Maths lessons, fostering a deep understanding of core mathematical principles, problem-solving strategies, and logical reasoning across all key stages.

  • Empower Confident Mathematicians: Develop students' resilience and self-belief in tackling challenging mathematical concepts, ensuring they are well-prepared for future academic and professional pursuits.

  • Spark Curiosity: Bring abstract mathematical ideas to life through practical applications, real-world examples, and challenging puzzles, igniting a genuine fascination with the subject.

  • Contribute to a Collaborative Department: Join a supportive and innovative Maths department that values teamwork, shared best practice, and a collective commitment to student success and academic excellence.

  • Thrive in a Diverse Community: Engage with students from a wide range of backgrounds in London, adapting teaching strategies to meet diverse learning needs and celebrating individual progress.

  • Shape Future Innovators: Play a key role in guiding A-Level students towards further study at leading universities and successful careers in STEM fields, finance, data science, and many other areas where strong mathematical skills are essential.

    We are looking for a passionate and qualified Mathematics teacher with a strong understanding of the Key Stage 3, GCSE, and A-Level Mathematics curricula. A proven ability to deliver exceptional lessons that inspire and challenge all learners is essential. If you are ready to make a significant contribution to our outstanding team in London across all key stages, we encourage you to apply for a September 2025 start.

    Inspire the next generation of mathematical thinkers with us in London – join our dedicated team for a September 2025 start, including A-Level teaching

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.