# Senior Director, Data Science at Coca-Cola

Coca-Cola is hiring a Senior Director, Data Science in Dublin, Ireland. Level rates it Works on AI ●●●○; you can [apply on Level](https://jobsbylevel.com/go/f4120816-6123-446f-91df-55c79eecff59).

AI Level 3, AI centrality 69 out of 100. Ireland - Dublin.

## Details

- Company: [Coca-Cola](https://jobsbylevel.com/companies/coca-cola)
- AI level: AI Level 3 (score 69 out of 100)
- Location: Ireland - Dublin
- Posted: October 8, 2026
- Apply: https://jobsbylevel.com/go/f4120816-6123-446f-91df-55c79eecff59

## Description

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. Role Purpose Lead the application of data science, decision science and AI across the Europe Operating Unit to deliver measurable business value. Partner with Functional Organisation leaders, markets, bottlers and Data & Intelligence teams to determine where advanced analytical techniques can improve decisions, automate work, strengthen planning and unlock new sources of growth or efficiency. Bring a strong understanding of how Europe’s data works across different markets, bottlers, customers, channels and ways of selling. Bridge to Global teams and enterprise capabilities where scale and standards add value, while ensuring Europe-specific data, context and business needs are reflected in use cases, models, methods and deployment choices. 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

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/senior-director-data-science-at-coca-cola-70313b

Source: https://jobsbylevel.com/jobs/senior-director-data-science-at-coca-cola-70313b

## Cite this page

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