Graduate Research Analyst

Cheltenham
9 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst – Payments Consulting

Data Analyst – Payments Consulting

Junior / Graduate Data Scientist

Data Analyst Graduate

Trainee Data Analyst/Support – Training Course

Data Science Placement Programme

Job Title -Graduate Research Analyst

An exciting opportunity for someone with research experience to work in an established project team working on behalf of some of their largest clients.

Responsibilities

  • Review and collate qualitative and quantitative data to produce reports and presentations suitable for the client's needs.
  • Design questionnaires and other necessary research materials for given projects.
  • Execute and manage multinational market research and consulting projects focused on medical technology innovation, product launch and marketing strategy.
  • Work as part of a project team analysing and interpreting qualitative and quantitative market research findings and translating these into actionable strategies for our clients.
  • Develop and be responsible for client reports / project outputs.

    The Candidate
  • Strong analytic and problem-solving skills
  • Capacity & desire to learn a range of analytic skills quickly.
  • Ability to deliver under pressure and to tight deadlines.
  • Relentless attention to detail
  • Ability to work within a team as well as independently.
  • Excellent written & oral communication skills
  • Ability to analyse and interpret market & market research data (Quantitative &
    Qualitative)
  • Produce PowerPoint reports of key insights.

    Central Cheltenham location, excellent starting salary and additional bonus.

    Travail Employment Group Ltd is acting as an Employment Agency in relation to this vacancy

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.

Careers

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.

Jobs

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.

Education

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.

🍪 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.