Senior Data Analyst

Whitley, Coventry
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Job Title: Senior Data Analyst (12-Month Rolling Contract)
Location: Whitley, UK (Hybrid: Generally 3 days on-site)
Employment Type: 12-Month Contract (Inside IR35 / Rolling)
Contract Type: Hybrid (3 Days onsite/ 2 days Onsite)
Sponsorship: No Sponsorship Provided (Must have full right to work in the UK)
  
The Opportunity:
The Business Performance Intelligence function within the Global Customer Care team has enabled a strong data capability model across the organization to leverage data-driven decision-making across all aspects of Parts and Accessories (P&A) Revenue performance and Customer Satisfaction. This role is part of the Retail Performance Intelligence (RPI) team, where our aim is to integrate data-driven insight into operations to drive highly bespoke and predictive customer service.
  
Key Accountabilities & Responsibilities

Combine data from multiple sources to generate new insights, identifying correlations of events and activities to inform future action plans at retailer and market levels.
Access a pool of information based on the outputs of 1,700 retailers worldwide to set the standard for insight generation.
Own the calculation of the retention metric and lead the objective toward an increasingly insightful way we measure how well we retain customers using transactional data.
As the RPI Intelligence lead, you will have full ownership of the “Ideas Hopper” generated through feedback from global regions and markets.
Work in close collaboration with TCS and the Systems and Data teams to ensure on-time delivery of agreed hopper items.
Apply a methodical approach to ensure on-time delivery of stakeholder requirements and the advancement of data capabilities with retail transactional data.
Monitor and report on trends, achievements, and intelligence best practices at the market level.   
Knowledge, Skills, and Experience
Essential:

Advanced knowledge of technologies, techniques, and practices to manage complex datasets and summarize key messages and recommendations.
High proficiency in Tableau is mandatory, including an eye for "Tableau artistry" (use of color, contrast, layout, and interactivity).
Previous experience and knowledge of various data management tools, specifically Google BigQuery or Enterprise Data Warehouses (EDW).
Skilled communicator with the ability to bridge the gap between technical and business communities to ensure a common understanding.
Experience working with customers to understand problem statements and translating them into clear requirements for delivery teams.
Proven experience in managing project delivery against strict deadlines.   
Desirable:

Working understanding of SQL, Anaplan, and JIRA.
Background in Agile delivery with the ability to develop customer-centric user stories aligned to product features.
Sound understanding of retailer processes acquired through Automotive or Luxury retail experience.
Understanding of predictive profiling.   
Key Performance Indicators (KPIs)

Identification of data-driven insights to drive Revenue and Customer Satisfaction.
Quarterly development of opportunities via the Intelligence tools/funnel.
Development of a roadmap to achieve insight generation based on transactional data.
P&A revenue and customer satisfaction improvement for identified underperforming retailers

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.