Consultant, Data Science

Support the analysis and delivery of defined Europe Data Science, Decision Science and AI use cases. Apply statistical, analytical and machine-learning techniques to prepare data, explore patterns, build and evaluate models, generate insights and support product delivery.

Over-index on practical AI capabilities to speed up analysis and execution, including AI-assisted exploration, coding, documentation, testing, synthesis and communication. Use these capabilities responsibly, with appropriate validation, data protection, human judgement and technical review.

Work within agreed scope, methods and standards, seeking guidance where the business question, technique, risk or delivery decision requires more senior judgement. Develop understanding of Europe’s markets, bottlers, customers, channels, ways of selling and regional or country-specific data so outputs are relevant and correctly interpreted.

Succes is measured by accurate, well-documented and timely analysis that supports Europe use cases, with AI used responsibly to accelerate delivery while maintaining quality, reproducibility and business relevance.

Complete defined analytical work packages within wider Data Science projects or products.

• Objectives, scope, methods, assumptions, outputs and timelines are clarified with the project or product lead.
• Data is explored, prepared and analysed using appropriate statistical, machine-learning or decision-support techniques.
• Progress, findings, limitations and issues are communicated early and clearly.
• Work is peer-reviewed and updated based on technical and business feedback.
• Tasks are delivered to agreed quality, documentation and reproducibility standards.

• AI is used to support activities such as code generation, query development, exploratory analysis, feature ideation, documentation, testing, synthesis and data storytelling.
• AI-generated code, analysis and content are independently checked, tested and validated before use.
• Confidential, personal, licensed and commercially sensitive data is handled only through approved tools and methods.
• The Consultant recognises where AI output may be incomplete, biased, inaccurate or unsuitable and escalates concerns appropriately.
• Reusable prompts, code patterns, analytical components and learning are captured where they can accelerate future delivery.

• Relevant differences in markets, bottlers, customers, channels, routes-to-market and ways of selling are considered in analysis and interpretation.
• Regional, bottler, syndicated, shopper, customer, consumer, financial and country-specific data are used appropriately within agreed access and usage rights.
• Differences in data coverage, quality, granularity, methodology and comparability are documented.
• Common analytical methods are applied consistently while preserving essential local context.
• Findings avoid over-generalising from one market or dataset to the whole of Europe.

• Features, models, experiments and analytical outputs are developed within the defined solution design.
• Models are evaluated using appropriate baselines, metrics, validation methods and sensitivity checks.
• Assumptions, uncertainty, limitations and potential bias are documented clearly.
• Code, notebooks, queries and outputs are organised, versioned and reproducible.
• The Consultant supports user testing, technical validation and business acceptance activities.

• Results are explained in clear language for technical and non-technical audiences.
• Visualisations and narratives focus on the business question, evidence, implications and limitations.
• Recommendations stay within the strength of the evidence and clearly identify where further analysis is required.
• Feedback from users and stakeholders is incorporated into analysis and product improvements.
• Measures of usage, adoption and value are supported where relevant to the use case.

• The Consultant collaborates with Data Scientists, Decision Scientists, Product Managers, Data Engineers, Governance specialists, Architects and business colleagues.
• Dependencies on data, engineering, access, governance and business input are identified and tracked.
• Agile ceremonies, technical reviews, documentation and team ways of working are followed consistently.
• Knowledge, code, methods and lessons are shared to improve reuse and team capability.
• Guidance is sought early when scope, analytical method, responsible-AI risk or business interpretation is unclear.

Apply approved AI tools and techniques to increase speed, consistency and productivity across the analytical workflow.

Ensure analysis reflects the regional and country context of the business question.

Contribute to model and analytical-product development using agreed technical approaches and standards.

Help connect analytical outputs to the decision or action they are intended to support.

Work effectively as part of cross-functional Data & Intelligence teams.

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Analytics or a related quantitative field.
• A relevant Master’s degree is beneficial but not required.
• Relevant learning or certifications in machine learning, AI, analytics, cloud platforms, Python, SQL, MLOps or responsible AI are advantageous.
• Demonstrated continuous learning in Generative AI, analytical methods and emerging Data Science technologies is expected.

• Around five years of relevant experience in Data Science, Advanced Analytics, Machine Learning, Decision Science, Statistics, AI or a related quantitative role.
• Practical experience using Python, SQL or equivalent analytical tools for data preparation, exploratory analysis, modelling and visualisation.
• Experience applying statistical analysis, predictive modelling, experimentation, classification, forecasting, optimisation or related methods to defined problems.
• Practical experience using generative AI or AI-assisted development tools to accelerate analytical work, with evidence of validating and improving generated outputs.
• Experience working with structured and unstructured data, including data-quality assessment and documentation of limitations.
• Understanding of model evaluation, feature engineering, hypothesis testing and reproducible analytical workflows.
• Experience collaborating in Agile, product or project delivery teams and working with Data Engineering or business stakeholders.
• Awareness of cloud-based analytics, MLOps, model lifecycle management and responsible AI is beneficial.
• Experience with European, multi-market, bottler, syndicated, shopper, customer, consumer, financial or third-party data is advantageous.
• Strong problem-solving, attention to detail, communication and continuous-learning skills.

• AI-Assisted Analytics and Delivery
• Data Science and Advanced Analytics
• Python and SQL
• Statistical Analysis and Hypothesis Testing
• Machine Learning and Predictive Modelling
• Exploratory Data Analysis
• Feature Engineering and Model Evaluation
• Data Visualisation and Storytelling
• Europe Regional Data Understanding
• Reproducible Analysis and Documentation
• Responsible AI Awareness
• Agile Cross-Functional Delivery

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We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.