Data Analyst

Shepherd's Bush Green
3 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Job Title: Data Analyst

Location: White City, Greater London

Remuneration: £18.00 - £19.00 per hour

Contract Details: Temporary, Full Time (12 months)

Responsibilities:

Join our team as an Entry-Level Data Analyst and contribute to the Regulatory and Compliance team! Your role will include:

Entering technical data into a government portal with precision.

Interpreting and analysing data to enhance processes and support decision-making.

Identifying patterns or inconsistencies within data sets.

Collaborating with other teams to boost data quality.

Defining new processes to improve data input accuracy.

Preparing reports and visualisation tools to showcase department progress.

Ensuring data quality and integrity through diligent auditing.

Qualifications:

Education: Bachelor's degree in a relevant field.

Technical Skills:

  • Proficient in Microsoft Excel (pivot tables, VLOOKUP, etc.).

  • Familiarity with at least one data visualisation tool (Tableau, Power BI, etc.).

  • Basic understanding of statistical concepts and analytical techniques.

    Soft Skills:

  • Exceptional attention to detail with a strong commitment to accuracy.

  • Strong analytical and problem-solving abilities.

  • Excellent written and verbal communication skills to convey technical findings.

  • Ability to work independently and juggle multiple tasks.

  • High level of curiosity and a proactive approach to learning regulatory challenges.

    Why Join Us?

    This is a fantastic opportunity for recent graduates eager to step into the world of data analysis. You'll work in a vibrant environment, just a 3-minute walk from White City train station, making your commute a breeze!

    If you're ready to dive into data and make a real impact, apply now! We can't wait to meet you!

    Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible recruitment process to support candidates of all backgrounds and all abilities to apply. Adecco is committed to building a supportive environment for you to explore the next steps in your career. If you require reasonable adjustments at any stage, please let us know and we will be happy to support you.

    Adecco acts as an employment agency for permanent recruitment and an employment business for the supply of temporary workers. The Adecco Group UK & Ireland is an Equal Opportunities Employer.

    By applying for this role your details will be submitted to Adecco. Our Candidate Privacy Information Statement explains how we will use your information - please copy and paste the following link in to your browser

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.