Data Analyst

Reigate
10 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Location: Reigate, Surrey (Flexible/Hybrid options available)
Contract Type: Permanent
Salary: £33,000 - £42,000 per annum, depending on experience

Are you a data enthusiast with a passion for turning numbers into actionable insights? Do you have a knack for managing teams and ensuring the highest quality of data? If so, we have an exciting opportunity for you!

The Role
As Data Team Lead, you will play a pivotal role in managing our talented team of Data Analysts. Your primary focus will be on overseeing data-driven projects, ensuring that all data is accurate and efficiently processed. Here's what you'll be doing:

Key Responsibilities:

Lead and manage the Data Analysts, handling staffing matters such as annual leave approvals and performance appraisals.
Establish and maintain a time-based register of data activities to streamline work allocation.
organise and chair meetings, ensuring effective communication and documentation.
Collaborate with Project Managers to set priorities and manage workloads.
Assist in the development of data validation and analysis procedures to enhance data quality.
Respond to ad-hoc queries from clients and analysts, providing clear and concise information.
Generate and deliver reports and survey data to clients as needed.
Support the team by undertaking Data Analyst tasks when required.

What We're Looking For
The ideal candidate will possess strong organisational skills and a solid understanding of structured data. You should be personable, possess excellent communication skills, and have a genuine willingness to help your team succeed. We value continuous learning and a positive, can-do attitude!

Essential Requirements:

Ability to commute to Reigate for the required hours.
A team player who thrives in collaborative environments.Desirable Skills:

Proficiency in MS Excel and a solid understanding of databases and spreadsheets.
A project management qualification would be a plus!

Why Join Us?

Flexible Hours: Enjoy a work-life balance with our flexible working hours.
Generous Leave: 26 days of annual leave plus bank holidays!
Career Growth: Opportunities for skill development and career progression based on your ambitions.
Benefits: Company pension scheme, Christmas bonus, and performance-related bonuses.

Ready to Take the Next Step?
If you're excited about the prospect of leading a team and making a real impact through data, we'd love to hear from you! Apply now and embark on a rewarding career with us!

Please submit your application, including your CV and a cover letter, to [insert application email/portal]. We look forward to welcoming you to our team!

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 explaining how we will use your information is available on our website

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.