Data Scientist

• Join a global enterprise SaaS organisation to help build and scale a data-driven marketing analytics capability.
• Lead the development of standardised reporting, dashboards, and performance frameworks that provide a trusted view of marketing performance across regions, business units, and channels.
• Drive the evolution from reactive reporting to proactive, insight-led analytics that supports executive decision-making.
• Partner with cross-functional teams to improve reporting, data quality, governance, and self-service analytics capabilities.
• Lead the development and delivery of executive dashboards, scorecards, and marketing performance reporting.
• Ensure reporting is accurate, timely, standardised, and aligned to agreed KPIs.
• Reduce manual reporting through automation, reusable reporting frameworks, and AI-enabled processes.
• Support the development of a centralised analytics environment using Microsoft Fabric and Power BI.
• Consolidate fragmented reporting into scalable, standardised dashboards and templates.
• Develop and maintain a central marketing KPI and metrics dictionary.
• Implement data validation processes and improve data quality across marketing systems.
• Support data governance and standard tracking frameworks.
• Enable self-service analytics by creating documentation, templates, and user training.
• Analyse marketing performance trends and deliver actionable recommendations to stakeholders.
• Provide insight across digital marketing, brand performance, and communications effectiveness.
• Collaborate across global teams to establish consistent reporting standards and shared analytics capabilities.

Marketing Analytics Lead

• Own the quality strategy, operating model, and execution plan for AI Data Services.
• Build and standardize QA frameworks, audit models, scorecards, sampling approaches, and acceptance criteria.
• Define critical, major, and minor error classifications across different data workflows.
• Lead quality governance for strategic customer programs, including reporting, calibration, escalations, and remediation.
• Partner with Operations, Program Management, Supply Chain, Solutions, and Sales to ensure quality requirements are clearly understood and operationally feasible.
• Develop root cause analysis, corrective action, and continuous improvement processes.
• Build quality dashboards, customer-facing reporting, and internal quality performance views.
• Support the creation of SOPs, reviewer guidelines, calibration materials, and QA documentation.
• Help improve delivery consistency, reduce rework, and strengthen customer confidence.
• Mentor and guide QA managers, auditors, reviewers, and quality specialists as the team scales.

Head of Quality, AI Data Services – Americas, EMEA

• Enhancing Models: Continuously improving existing models by incorporating new features, fine-tuning parameters, and utilizing additional data.
• Algorithm Development: Collaborating with our Machine Learning Engineering team to develop and deploy scalable supervised and unsupervised learning algorithms.
• Data Exploration: Identifying and exploring new data sources while researching innovative ways to leverage both new and existing data effectively.
• Solution Research: Investigating new Machine Learning solutions to optimize every step of our value chain.
• A/B Testing: Regularly conducting and evaluating A/B tests to drive insights and refine our strategies.

Head of Quality, AI Data Services

• Research and develop data science methods that improve NEXUS predictive performance across diverse enterprise datasets, industries, and prediction task types
• Design and implement robust, production-quality Python components with a strong focus on correctness, generality, and reusability
• Deeply understand the characteristics of real-world enterprise data and develop strategies that help NEXUS handle them reliably
• Run rigorous experiments to measure the impact of new approaches, design meaningful benchmarks, and use results to guide prioritization
• Work across a wide variety of structured data problems - including but not limited to classification, regression, ranking, and forecasting
• Collaborate closely with the Engineering and Research teams to develop a deep understanding of NEXUS model behavior and use that knowledge to inform your work
• Work with Applied AI Engineers to validate approaches on real customer datasets and translate findings into product capabilities
• Contribute to technical documentation and internal best practices, helping the broader team apply new capabilities correctly and confidently

Data Scientist – Extensions

• Data literacy made easy
• Preparation for a career in Data Science, Business Analytics and AI
• Practice-oriented training in Business Analytics, Data Science & Data Engineering
• Hands-on practical projects instead of dry theory

Data Scientist jobs in Europe

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Marketing Analytics Lead

Head of Quality, AI Data Services – Americas, EMEA

Data Scientist

Head of Quality, AI Data Services

Data Scientist – Extensions

Data Science, Business Analytics