Procurement Senior Data Analyst (Supervisor)

Publicis Re:Sources
London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Quality & Systems Manager

Data Quality & Systems Manager

Data Quality & Systems Manager

Senior Data Scientist

RQ1688775 - Data Quality and Systems Manager

Data Compliance Lead

An opportunity has arisen for a Procurement Senior Data Analyst (Supervisor), to join the Publicis Groupe Procurement team. The Procurement team is a board-mandated function providing holistic procurement business solutions to the companies within Publicis Groupe. The department’s motto is to Protect, Enrich, Challenge and Reduce Publicis Groupe spend via best-in-class procurement processes and negotiations. The department manages full cycle, from sourcing to vendor and contract management.

This role can be based in either our London or Dublin office. Please only apply if you have the relevant right to work.

Position Summary

The role of the Global Procurement Senior Data Analyst (Supervisor) is pivotal in shaping and upkeeping various Procurement Data Sources, such as Spend Analysis, Portfolio KPIs, Saving KPIs, Spend Management Reporting, and Business Intelligence Reporting. This position entails a diverse range of responsibilities, including reporting, savings analysis, as well as overseeing invoicing and budget management.

Key Responsibilities

Provide Actionable Spend Analysis for the Procurement Team:

  • Develop comprehensive reporting to provide accurate insights into spend categories, markets, hubs, vendors, and spend types.
  • Standardise and optimize procurement data for streamlined review, consolidating data from various formats.

Manage and Verify Savings/Cost Avoidance Claims:

  • Thoroughly validate and confirm all submitted savings and cost avoidance claims, maintaining a centralized database.
  • Deliver real-time tracking of achieved savings for auditing purposes, supported by actualization schedules.
  • Report on key performance indicators, including savings, yields, managed spend, project volume, and delivery speed.

Analyse Procurement Pipeline and Portfolio Status:

  • Analyse the procurement pipeline and provide insights on portfolio status.
  • Maintain and update completed engagements in the business plan for performance tracking.
  • Collaborate with team leads to develop performance reports for markets, hubs, and agency overviews.
  • Identify trends, changes, updates, risks, and opportunities through data analysis.

Investigate and Analyse Existing Spend:

  • Conduct in-depth analysis of existing spend using ERP data, contract evaluations, and vendor assessments.
  • Maintain comprehensive datasets related to spend and performance.

Governance Reporting at Board Level:

  • Deliver board-level presentations highlighting progress against targets and Procurement KPIs.
  • Consolidate, validate, and prepare necessary communications, including board-level presentations.
  • Create and maintain dashboards within Business Intelligence tools to accurately represent source data for various teams.

Manage Data Analyst:

  • Prioritize and manage data analysts in execution of the performance activities for Board reporting & Procurement strategies monitoring.

Required Skills/Experience

  • Must be a Microsoft Excel expert
  • Solid financial understanding
  • Having a procurement experience is a plus
  • Minimum 3-5 years’ experience
  • Highly organised and able to prioritise tasks
  • Ability to build and maintain effective working relationships
  • A proven team player with excellent communication skills, analytical skills and attention to detail
  • Completely literate in English language and ideally one or more foreign language skills

Qualifications and Certifications

  • Educated to degree level or equivalent

Seniority level

  • Seniority levelAssociate

Employment type

  • Employment typeFull-time

Job function

  • Job functionAnalyst
  • IndustriesAdvertising Services

Referrals increase your chances of interviewing at Publicis Re:Sources by 2x

Get notified about new Data Analyst jobs in London Area, United Kingdom.

London, England, United Kingdom 9 months ago

London, England, United Kingdom 1 week ago

City Of London, England, United Kingdom 1 day ago

Analytics & Insights Professional, Analytics & Insights, Amazon Ads

London, England, United Kingdom 1 week ago

Business Intelligence & Visualisation Analyst - Retail Media

London, England, United Kingdom 2 months ago

Greater London, England, United Kingdom 2 weeks ago

London, England, United Kingdom 1 week ago

London, England, United Kingdom 5 days ago

London, England, United Kingdom 2 weeks ago

London Area, United Kingdom £35,000.00-£42,000.00 23 hours ago

London, England, United Kingdom 1 day ago

Leeds, England, United Kingdom 1 week ago

Feltham, England, United Kingdom 6 days ago

London, England, United Kingdom 4 weeks ago

London, England, United Kingdom 8 hours ago

London, England, United Kingdom 1 month ago

Analytics and Insight Professional, Amazon Ads, Amazon

London, England, United Kingdom 3 weeks ago

London, England, United Kingdom 1 week ago

London, England, United Kingdom 5 days ago

London, England, United Kingdom 6 days ago

London, England, United Kingdom 1 month ago

London Area, United Kingdom £60,000.00-£65,000.00 3 hours ago

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.


#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.