Data Manager

About the Position

We’re looking for a Data Manager to lead our Data Services team in Luxembourg. This is a senior player-manager role at the intersection of technical leadership, commercial delivery, and client engagement. You’ll lead a cross-functional team of Data Scientists, Senior BI Engineers, and Data Analysts, and serve as the primary point of contact for internal stakeholders and external clients, from scoping and prioritization through execution and impact.

Responsibilities

• Team Leadership & People Management

  • Lead, mentor, and develop the team, fostering a culture of technical excellence, collaboration, and continuous improvement.
  • Set clear priorities, allocate workload, and remove blockers so the team can do its best work.
  • Run regular 1:1s, performance reviews, and career development conversations.
  • Champion an AI-assisted working culture, encouraging intelligent use of AI tools while holding the bar on rigor, validation, and engineering quality.
  • Create an environment where team members own their domains and grow toward greater autonomy and seniority.

• Agile Delivery & Team Rhythm

  • Introduce and sustain light Scrum practices sized to the team, lean and purposeful, never bureaucratic.
  • Own the team’s JIRA workflow: groomed backlog, clear and actionable tickets, transparent tracking from intake to delivery.
  • Facilitate standups, sprint planning, reviews, and retrospectives, focused, time-boxed, and genuinely useful.
  • Use sprint cadences to create predictable delivery rhythms and give stakeholders reliable visibility into progress.
  • Continuously refine team processes based on retrospective learnings, velocity patterns, and stakeholder feedback.

• Business & Client Engagement

  • Act as the primary point of contact for internal stakeholders (Media, Content, Account teams) and external clients on all Data Services requests.
  • Lead discovery and scoping, translating ambiguous business questions into structured problem statements, delivery plans, and success criteria.
  • Maintain a prioritized backlog, making transparent trade-offs that balance client urgency, strategic value, and team capacity.
  • Manage stakeholder expectations across the delivery lifecycle, communicating progress, risks, and outcomes clearly and confidently.

• Hands-On Technical Contribution

  • Stay an active technical contributor, building, reviewing, debugging, and fixing across the team’s stack when needed.
  • Apply hands-on skills across SQL, Python, dbt, BigQuery, AWS, Power BI, and Looker Studio, shipping production-quality work yourself, not just advising.
  • Define and enforce technical standards across data modeling, analytics, visualization, and ML/statistical approaches.
  • Partner with IT on data infrastructure, system integrations, and the technical enablement needed to deliver reliably at scale.

• AI-Assisted Development, Accelerated, Not Undisciplined

  • Use AI coding and reasoning assistants (GitHub Copilot, Cursor, Claude, ChatGPT) to write, refactor, and review SQL, Python, dbt models, and analytical outputs, as force multipliers on a strong technical foundation.
  • Leverage automation and orchestration tools (n8n, LangGraph, Make, or similar) to eliminate repetitive work in data processing, reporting, and workflow management.
  • Apply disciplined practices: clear prompting, thorough code review, systematic testing, rigorous debugging, never shipping AI output without understanding it.
  • Bring an ML and AI lens to client problems, identifying where predictive models, recommendation logic, NLP, anomaly detection, GenAI, or LLM-based approaches can create real business value, and leading the team to operationalize them responsibly.
  • Critically evaluate emerging AI/ML tools with pragmatism and a clear eye on quality, explainability, and client trust.

• Thought Leadership & Research Agenda

  • Drive the intellectual direction of the Data Science function, identifying high-value research areas from client signals, business priorities, and market developments.
  • Define and assign research projects to the Data Scientist, well-scoped, commercially grounded, and translatable into repeatable solutions.
  • Stay current on applied ML, LLMs, retail media analytics, and AI tooling, bringing relevant innovations into team practice with a grounded, evaluated approach.
  • Champion rigorous, explainable, and responsible AI, where speed comes from AI tools, but quality is guaranteed by human expertise.

• Commercial Solution Development

  • Lead the development of new intelligence products and analytical solutions, ML models, AI-powered tools, and advanced analytics that can be packaged and scaled across the client portfolio.
  • Collaborate with Account, Media, and Content leadership to identify unmet client needs that data, ML, and AI can address commercially.
  • Build compelling solution narratives, from problem framing through methodology to business impact.
  • Present data and intelligence solutions in client pitches and capability presentations, translating technical concepts for non-technical audiences.

• Delivery Excellence & Cross-Functional Coordination

  • Own end-to-end delivery quality for the team’s outputs, dashboards, models, reports, tools, and analyses, ensuring they’re accurate, reliable, and useful.
  • Facilitate smooth collaboration across Data Science, BI Engineering, and Data Analytics disciplines and with IT, Media, and Content functions.
  • Act as the escalation point for quality issues, methodology questions, and delivery risks, and resolve them directly when needed.

Requirements

• 5+ years of experience in Data Science, Advanced Analytics, or a related field, with at least 1-2 years in a leadership role overseeing mixed technical teams.
• Strong technical fundamentals: advanced SQL, solid Python (pandas, numpy, scikit-learn or equivalent), familiarity with cloud data environments (BigQuery, AWS), and a clear understanding of how data systems work end-to-end.
• Solid working knowledge of ML and AI: core ML concepts (supervised/unsupervised learning, classification, regression, clustering, recommendation, anomaly detection), critical evaluation of model quality, and judgment on when and how to apply ML/AI to real business problems.
• Demonstrated AI-assisted working practice, active, intelligent use of AI coding assistants and automation tools (GitHub Copilot, Cursor, Claude, ChatGPT, n8n, LangGraph) with proper review, testing, and validation discipline.
• Experience running light Agile/Scrum practices in a data or analytics team, JIRA administration, backlog management, sprint facilitation, and standups.
• Genuinely hands-on, you can independently write, debug, and ship production-quality work, and step in across the team’s full stack when needed.
• Proven ability to lead client-facing engagements, scoping, managing expectations, presenting solutions, and building trust with technical and non-technical stakeholders.
• Commercial acumen, turning analytical and ML/AI work into scalable, repeatable products or service offerings.
• Strong applied knowledge of statistics, measurement, and experimentation, with the ability to defend methodology and model choices in a client context.
• Excellent communication skills, translating technical and AI complexity into clear narratives for business leaders, clients, and executive audiences.
• Fluent English, written and spoken.

Nice to Have

• Experience in digital advertising, retail media, or e-commerce, particularly the Amazon ecosystem (AMC, DSP, Sponsored Ads, Vendor Central).
• Familiarity with Amazon Marketing Cloud (AMC) data structures, clean-room constraints, and audience/measurement patterns.
• Hands-on experience with GenAI / LLM-based solutions: prompt engineering, RAG pipelines, LLM evaluation, or human-in-the-loop deployment.
• MLOps experience, model versioning, monitoring, drift detection, and operationalization of decision logic into production workflows.
• Background building internal data products, self-service tools, semantic layers, scoring systems, or recommendation engines.
• Exposure to WPP, agency, or consulting environments where client delivery and commercial growth run in parallel.
• Additional languages, especially French or German.

We Offer

• A senior leadership role with real strategic influence over how data, ML, and AI are used across one of Europe’s leading Amazon practices.
• The opportunity to build and shape a team, define its roadmap, and develop commercial intelligence solutions that directly impact client outcomes.
• A culture that embraces modern AI-assisted ways of working, empowered to experiment, automate, and move fast, backed by strong technical foundations.
• Close collaboration with top global brands and some of the most advanced Amazon advertising setups in Europe.
• A collaborative, fast-moving environment where curiosity, ownership, and impact are genuinely valued.
• An inclusive workplace that respects individuality and values diverse perspectives.

About the Company

VML is a global creative company that combines brand experience, customer experience, and commerce to create connected brands that drive growth. Guided by a Human First philosophy, VML is known for its innovative work for brands and consistently ranks among the world’s leading creative companies, including the WARC Creative 100. VML’s global network spans 55+ markets with principal offices in Buenos Aires, Kansas City, London, New York, São Paulo, Shanghai, and Singapore.

VML is part of WPP, the trusted growth partner for the world's leading brands. Powered by exceptional talent and our agentic marketing platform WPP Open, WPP unites cutting-edge media intelligence and data solutions, creativity, production, enterprise solutions, and expert strategic counsel.

For more information, please visit www.vml.com and follow along on LinkedIn #WeAreVML.