Data Analyst - Certification schemes

Element Materials Technology Ltd.
High Wycombe
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Overview

Element is currently seeking to hire a Data Analyst to join our team on an 18 month fixed term contract, supporting our certification business.


The Data Analyst will support the development of existing and new schemes by leveraging internal and publicly available data. This role is critical in driving informed decision-making, identifying trends, and optimizing business performance through advanced data analysis and visualization.


Responsibilities

  • Collaborate with project managers to understand analytics needs, define KPIs, and deliver actionable insights
  • Proactively analyze data to answer key business questions and identify areas for improvement in efficiency and productivity
  • Develop and maintain interactive visualizations and dashboards using data from multiple sources.
  • Define and implement data acquisition and integration logic for scalability and performance
  • Build and maintain databases, acquire data from primary and secondary sources, and develop scripts for flexible evaluation processes
  • Provide forecasting, capacity planning, and operational metrics to support business growth

Skills / Qualifications

  • Minimum 3 years of experience as a Data Analyst
  • Understand and utilized data that supports BAU (Business as Usual) operations to inform strategic decisions
  • Identify trends and opportunities for growth through analysis of complex datasets
  • Evaluate organizational methods and data to improve efficiency and accuracy
  • Assess risk and prioritize actions based on data insights
  • Create best-practice reports and visualizations to communicate findings effectively
  • Strong analytical skills, including data mining, evaluation, and visualization
  • Proficiency in SQL and Excel, with the ability to learn other analytics tools
  • Bachelor’s degree (or equivalent) in Mathematics, Computer Science, Economics, or Statistics
  • Experience with database and model design, segmentation techniques, and TIC industry knowledge
  • Programming experience with relevant frameworks
  • Practical experience in statistical analysis using tools such as Excel, SPSS, and SAS
  • Proven success in collaborative, team-oriented environments

Benefits of working at Element


33 days annual holiday, consisting of 25 days annual holiday and 8 days public / x4 Life Assurance / Legal & General Pension scheme with total contributions up to 12% / Flexible Working / Cycle Scheme / Free Onsite Refreshments / Recommend a Friend Bonus / Perks At Work Discount Scheme


For more information on Element's BUILT division, please take a look at our e-brochure: https://indd.adobe.com/view/7032c5bd-3645-4c48-86d4-b568d53de400


To apply please email


Company Overview

Element is one of the fastest growing testing, inspection and certification businesses in the world. Globally we have more than 9,000 brilliant minds operating from 270 sites across 30 countries. Together we share an ambitious purpose to ‘Make tomorrow safer than today’.


When failure in use is not an option, we help customers make certain that their products, materials, processes and services are safe, compliant and fit for purpose. From early R&D, through complex regulatory approvals and into production, our global laboratory network of scientists, engineers, and technologists support customers to achieve assurance over product quality, sustainable outcomes, and market access.


While we are proud of our global reach, working at Element feels like being part of a smaller company. We empower you to take charge of your career, and reward excellence and integrity with growth and development.


Industries across the world depend on our care, attention to detail and the absolute accuracy of our work. The role we have to play in creating a safer world is much bigger than our organization.


Diversity Statement

At Element, we always take pride in putting our people first. We are an equal opportunity employer that recognizes diversity and inclusion as fundamental to our Vision of becoming “the world’s most trusted testing partner”.


All suitably qualified candidates will receive consideration for employment on the basis of objective work related criteria and without regard for the following: age, disability, ethnic origin, gender, marital status, race, religion, responsibility of dependents, sexual orientation, or gender identity or other characteristics in accordance with the applicable governing laws or other characteristics in accordance with the applicable governing laws.


The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60-1.35(c)


“If you need an accommodation filling out an application, or applying to a job, please email ”


#J-18808-Ljbffr

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.