Principal Data Scientist

Job description

Job TitlePrincipal Data Scientist Job DescriptionJob title: Principal Data Scientist Your role: The Performance Analytics & AI Business Partner combines deep commercial understanding with strong hands-on expertise in data science, machine learning, Generative AI and agentic AI. The role is accountable for turning complex performance data into trusted decisions, scalable analytics products and intelligent tools that help Business Units and Regions sense, diagnose, predict and act faster. The role owns the performance-analytics foundation: sell-out and market data from syndicated vendors such as Circana, GfK, Nielsen and IQVIA; performance dashboards; data definitions; and the business narrative behind the numbers. It also leads the evolution beyond traditional reporting by building advanced analytical models, AI agents and conversational decision-support tools. This is not a reporting role and not a purely technical data-scientist role. It requires someone who can build robust analytical solutions, understand the underlying data and models, and confidently influence senior commercial leaders with clear “so what / now what” recommendations. Job Responsibilities: 1. Performance Analytics Business Partnering Act as the day-to-day performance analytics partner for assigned Businesses and Regions. Translate leadership questions into clear analytics, data-science and AI solution requirements. Lead analysis across market share, sell-in, sell-out, revenue, margin, price, mix, distribution, customer and channel performance. Support business reviews, planning cycles, forecasts and strategic discussions with fact-based insights and recommendations. Connect multiple performance signals to identify underlying drivers, risks, opportunities and required actions. Challenge assumptions constructively and translate complex analysis into concise executive narratives. 2. Syndicated Data, Data Foundations and Governance Own the business use, interpretation and quality of syndicated market and sell-out data from Circana, GfK, Nielsen, IQVIA and other relevant vendors. Understand and manage data methodologies, coverage, definitions, assumptions and limitations. Work with Data & Technology, data engineering and external providers to integrate, validate and harmonise data in ADL and related platforms. Establish robust reconciliation, validation and quality-control processes to ensure performance outputs are trusted and first-time-right. Define common business rules, hierarchies, calculations and performance definitions across Businesses and Regions. Document data logic, methodologies and known limitations clearly. 3. Dashboards and Analytics Products Own and continuously improve sell-out and commercial performance dashboards as analytics products, not static reports. Build scalable, reusable dashboards and automated analytical workflows using SQL, Python and modern BI tools. Move dashboards beyond reporting by embedding automated diagnostics, alerts, benchmarks, root-cause analysis and recommended actions. Lead requirements gathering, design, user testing, deployment and adoption of analytics products. Simplify the dashboard landscape by eliminating duplicate, manual and low-value reporting. Monitor solution usage, user feedback, data quality and business impact, and continuously improve the product backlog. 4. Advanced Analytics and Data Science Analyse complex, high-volume structured and unstructured datasets using robust statistical and data-science methods. Develop and deploy predictive and diagnostic models for commercial performance questions, including forecasting, regression, classification, clustering, anomaly detection, price and mix analysis. Own the end-to-end data-science lifecycle: problem framing, data preparation, feature engineering, model development, validation, deployment, monitoring and improvement. Assess model performance, stability, explainability and business relevance before scaling solutions. Apply scientific rigour to distinguish correlation, decomposition and causal insight; ensure models are used appropriately in decision-making. Write clean, documented and reusable code, and partner with technical teams to productionise analytical solutions. 5. AI, GenAI and Agentic Decision Intelligence Design, build and deploy AI-enabled tools, Generative AI applications and analytical agents for performance management. Use LLMs, retrieval-based approaches, machine learning and automation to enable users to ask business questions in natural language and receive trusted, grounded answers. Create agents that can monitor performance, identify deviations, retrieve relevant context, diagnose likely drivers, simulate scenarios and recommend next-best actions. Combine dashboards, predictive models and conversational AI into intuitive decision-support experiences. Rapidly prototype AI use cases, test them with users and scale successful solutions into robust products. Apply responsible-AI principles, including data security, access control, traceability, explainability, evaluation, bias awareness and clear human decision rights. Stay current on developments in AI, LLMs, agentic frameworks and data science, applying innovations pragmatically where they create measurable value. 6. Technology, Deployment and Solution Architecture Partner with IT, data engineering, software development and platform teams to ensure analytical and AI solutions are secure, scalable, reliable and aligned with enterprise standards. Define technical requirements, support solution architecture, testing and production deployment. Establish appropriate model monitoring, version control, documentation and evaluation processes. Resolve data and technical issues that affect the reliability, speed or usability of analytics products. Balance rapid MVP delivery with sound product-engineering fundamentals. 7. Adoption and Capability Building Drive adoption of dashboards, models and AI tools through training, coaching, clear playbooks and active engagement with business users. Build confidence in data, analytical methods and AI recommendations by making logic and limitations transparent. Help stakeholders use performance insight consistently in business reviews, planning, customer discussions and commercial decisions. Share best practices across Businesses, Regions and analytics teams. Track whether insights and AI tools lead to decisions, actions and measurable business impact. Success Measures Trusted, consistent and timely sell-out and market-performance insights across Businesses and Regions. High adoption and demonstrable impact of dashboards, analytics products and AI-enabled tools. Reduced manual reporting and duplicate analytics solutions. Earlier identification of performance risks and growth opportunities. Increased use of predictive analytics, AI agents and automated diagnostics in regular business routines. Strong data quality, governance, model reliability and responsible-AI compliance. Clear evidence that analytical recommendations translate into business decisions and commercial impact. Positive feedback from senior Business, Regional, Sales, Marketing and Finance stakeholders. You're the right fit if: Experience Preferably 9+ years of experience in performance analytics, commercial analytics, data science, decision intelligence, business intelligence or a related field. Proven experience with syndicated market or sell-out data, such as Circana, GfK, Nielsen, IQVIA or equivalent. Demonstrated experience building and deploying analytics products, dashboards, predictive models or AI-enabled tools—not only consuming reports. Strong experience partnering with senior commercial stakeholders and influencing decisions without direct authority. Experience working across multiple markets, categories or Business Units in a complex matrix organisation. Experience taking analytical solutions from business problem through build, deployment, adoption and measurable impact. Technical and A