Junior Data Analyst

Oxford
1 month ago
Applications closed

Related Jobs

View all jobs

Junior Data Analyst

Junior Data Analyst

Junior Data Analyst

Junior Data Analyst

Junior Data Analyst

Junior Data Analyst

A leading provider of catering and support services to the education sector in Oxford are seeking a talented and detail-oriented Junior Data Analyst to join their team.

KEY DUTIES

  • Collect, clean, and analyse large datasets to uncover trends, patterns, and insights.

  • Develop and maintain data pipelines and workflows to ensure data integrity and accessibility.

  • Design and implement data models and visualizations to communicate findings to stakeholders.

  • Collaborate with cross-functional teams to identify business requirements and translate them into data analysis solutions.

  • Conduct ad-hoc analyses and deep dives to support business initiatives and strategic decisions.

  • Monitor and evaluate the performance of data-driven initiatives and recommend optimizations.

  • Stay current with industry trends and best practices in data analytics and contribute to continuous improvement efforts.

    CANDIDATE REQUIREMENTS

  • Independent skills

  • Team work skills

  • Organisational skills

  • Good written and oral communication skills

  • Self-Motivated

  • Meets the eligibility below

    Ideal but not essential

  • Since the role is office based and not remote, you be living in the Oxfordshire region

    ELIGIBILITY

  • Have the right to live and work in the UK.

    Sound like you? Then send us an application and we will let you know if you are suitable for this position, or one of the other roles we have available

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.