Senior Director, Data Science

Job Description Summary The Senior Director, Data Science Europe is part of the Europe Data & Intelligence leadership team and reports to the Senior Director II, Data & Intelligence Europe. The role operates as part of one integrated team with a shared mission, common priorities and connected ways of working across Data Product Management, Data Governance, Data Engineering, Decision Intelligence and AI.

Key Accountabilities

1. Drive EOU Value Through Data Science, Decision Science and AI
   
   Identify and lead the highest-value opportunities where data science, decision science and AI can improve business decisions and performance across Europe.
   
   What success looks like
   
   Use cases are directly connected to EOU priorities, Functional Organisation objectives and measurable business outcomes. Business challenges are translated into appropriate analytical questions, decision frameworks and solution pathways. Data science and AI investments are prioritised based on value, feasibility, data readiness, adoption potential and ability to scale. Solutions improve decision quality, productivity, commercial effectiveness, customer or consumer understanding, planning or operational performance. Value, adoption and performance are measured throughout the lifecycle rather than only at delivery.
2. Apply the Right Science to the Right Business Decision
   
   Establish a disciplined approach for deciding when to use data science, decision science, advanced analytics, experimentation or simpler analytical methods.
   
   What success looks like
   
   Teams distinguish clearly between descriptive, diagnostic, predictive, prescriptive and decision-support needs. Data science techniques are applied where patterns, prediction, optimisation, classification, experimentation or automation can create incremental value. Decision science methods are applied where leaders need structured choices, scenarios, trade-offs, causal understanding, uncertainty assessment or optimisation of decisions. Teams avoid unnecessary technical complexity and select methods proportionate to the business question, available data and decision context. Model outputs are translated into clear recommendations, choices and actions that business users can understand and apply.
3. Build on Europe's Regional Data and Business Context
   
   Ensure advanced analytics and AI solutions reflect how data and the business operate across Europe's diverse markets and bottling system.
   
   What success looks like
   
   A clear view is maintained of data availability, ownership, quality, granularity, comparability and accessibility across Europe markets and bottlers. Differences in market structures, customers, channels, routes-to-market and ways of selling are reflected in analytical design and interpretation. Internal, bottler, syndicated, customer, consumer, financial and external data are combined appropriately to answer priority business questions. Regional limitations, biases and gaps are understood before models are developed or scaled. Common methods and metrics are used where they add consistency, while local context is retained where it is essential to relevance and accuracy. Europe-specific context and intellectual property are captured so they can be governed, reused and applied in future AI and decision intelligence experiences.
4. Bridge Global Capability and Regional Specificity
   
   Represent Europe's data science and decision science needs within the Global organisation and maximise the value of enterprise capabilities for the EOU.
   
   What success looks like
   
   Global frameworks, platforms, methods and reusable assets are adopted where they meet Europe's needs and create scale. Europe-specific requirements, data realities and use cases are represented in Global roadmaps and capability discussions. Clear choices are made on when to adopt, adapt, enrich or build based on value, reuse, regional relevance and total cost. Regional innovations and learnings are shared back with Global teams and other Operating Units. Enterprise investments generate greater value through regional activation, adoption and measurable use.
5. Establish Frameworks, Standards and Responsible Practice
   
   Create a consistent and pragmatic approach to data science and decision science across the EOU.
   
   What success looks like
   
   Recognised industry frameworks and maturity models are used to assess capability, prioritise gaps and guide pragmatic development roadmaps. Common practices are established for problem framing, experimentation, model design, validation, explainability, deployment, monitoring and lifecycle management. Responsible AI, privacy, security, model risk, fairness and appropriate human oversight are embedded in delivery. Analytical work is reproducible, transparent and supported by clear documentation and ownership. Standards accelerate delivery and reuse without creating unnecessary process or reducing local relevance.
6. Lead One Data Science Team and Capability
   
   Build a high-performing Data Science capability that operates as an integrated part of the Europe Data & Intelligence organisation, bringing together the right people, partners and cross functional teams around priority EOU outcomes.
   
   What success looks like
   
   The team works to a shared EOU mission, common priorities and connected product and data roadmaps, collaborating actively across the wider EOU Data & Intelligence team. Data Scientists and Decision Scientists are brought together with Product Management, Data Engineering, Governance, Architecture and business facing colleagues in outcome led, cross functional teams focused on the highest value EOU opportunities. Clear accountabilities, performance expectations, career pathways, succession plans, coaching and development opportunities strengthen individual performance and the talent pipeline. Ways of working encourage technical excellence, business curiosity, experimentation, peer review, reuse, continuous learning and shared accountability across disciplines. The team partners seamlessly across the EOU organisation from discovery through run and optimisation, creating high performing teams with clear outcomes, complementary skills and effective decision rights. Workforce capacity, internal capability, strategic partners and enterprise resources are managed deliberately to create sustainable delivery, knowledge transfer and value for the EOU. Budget, headcount and partner spend are planned and managed transparently, aligned to agreed EOU priorities and expected value. Resources are allocated and reallocated across Run, Optimise and Build based on business value, urgency, specialist capability, delivery capacity and readiness to scale. Demand, capacity, skills and delivery risk are reviewed regularly, with clear trade offs and recommendations escalated through the Europe Data & Intelligence leadership team. Financial performance, resource utilisation and partner outcomes are monitored to improve efficiency, accountability and return on investment.
7. Scale, Operate and Optimise Analytical Products
   
   Move priority solutions beyond experimentation into trusted, adopted and sustainable business capabilities.
   
   What success looks like
   
   Proofs of concept have explicit criteria for fast tracking, scaling, transitioning or closing based on value, urgency, evidence, data readiness, adoption potential and operational feasibility. Priority opportunities are fast tracked where speed creates material EOU value and the risks are understood; proven solutions are scaled where there is repeatable value, sufficient readiness and a sustainable operating model. Performance, drift, adoption, value and operational health are monitored after launch. Solutions are continuously optimised based on business feedback, data changes and observed outcomes. Reusable models, features, methods and components reduce duplication and accelerate future delivery across markets, functions and bottlers.

Education Requirement Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Operations Research, Economics, Engineering or a related field; a Master's degree or doctorate is strongly preferred. Relevant professional learning in AI, machine learning, decision science, optimisation, experimentation, cloud platforms, product management or responsible AI is beneficial. Equivalent combinations of advanced technical expertise, leadership experience and demonstrated business impact may be considered.

Skills and Experience You Need 12+ Years Extensive experience in data science, decision science, advanced analytics or artificial intelligence, including progression into leadership roles. Proven experience delivering scalable analytical or AI solutions that create measurable business value in complex, multi market organisations. Deep expertise in machine learning, statistical modelling, experimentation, causal inference, optimisation and analytical decision support. Experience determining when advanced methods are appropriate and when simpler analysis or business rules will deliver better value. Strong understanding of data engineering dependencies, modern data platforms, cloud ecosystems, MLOps and production deployment. Experience working with business, product, engineering, governance, architecture and change teams in product centric or Agile operating models. Demonstrated ability to explain analytical methods, uncertainty and recommendations to non technical and senior stakeholders. Experience establishing standards for model development, validation, responsible AI, deployment, monitoring and lifecycle management. Leadership experience managing people, allocating resources, building teams . click apply for full job details