Senior Data Scientist at The Heineken Company

• Drive the development and deployment of production-grade machine learning and artificial intelligence solutions that generate measurable business value across the organisation.
• The Senior Data Scientist acts as a technical leader who bridges data science innovation with operational deployment, ensuring analytics solutions are scalable, maintainable, and embedded in business processes.
• This role combines deep technical expertise in MLOps and production pipelines with strong stakeholder management, mentoring junior team members while delivering advanced analytics solutions.

Key responsibilities:

• Design, build and deploy production-grade machine learning and artificial intelligence solutions and products for business specific use cases
• Design, build and deploy end-to-end machine learning pipelines from data ingestion through model training to production inference and monitoring; ensuring solutions meet enterprise standards for reliability and performance.
• Implement MLOps best practices including CI/CD automation with testing, validation, monitoring and deployment strategies.
• Implement A/B testing frameworks to validate model improvements in production environments and measure incremental business impact
• Conduct code reviews to ensure quality, maintainability, and adherence to team standards, providing constructive feedback that develops team capabilities.
• Mentor and coach junior data scientists in technical skills including Python programming, machine learning algorithms, MLOps best practices, and production deployment patterns.
• Develop reusable Python packages for common machine learning workflows with robust dependency management, versioning, and automated updates to ensure consistency and security across production pipelines.
• Build strong relationships with regional and global analytics leadership, data and business stakeholders and product owners to facilitate cross-functional collaboration.
• Present complex technical concepts and model results to non-technical audiences including C-suite executives, using clear visualisations and business-focused narratives that drive decision-making.

Education and Experience: 

• Bachelors Degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, Physics, or related quantitative field required.
• Master’s degree preferred.
• 5+ years in a technical analytics or data science environment

Must have (all levels):

Production machine learning and technical expertise:

• Proficient in Python with strong software engineering practices including unit testing , integration testing, version control, code reviews, and documentation.
• Experience deploying ML models to production with automated CI/CD pipelines, monitoring, and retraining workflows.
• Experience implementing monitoring for production ML systems including data quality checks, model performance metrics, drift detection, and alerting.
• Knowledge of containerisation and model deployment orchestration strategies.
• Experience with at least one major cloud platform (Azure strongly preferred given Databricks integration, AWS or GCP acceptable) including compute, storage, and managed services.

Machine Learning & Analytics:

• Strong foundation in statistical methods, machine learning algorithms and model evaluation techniques.
• Practical knowledge of model validation, cross-validation strategies, holdout test design, and A/B testing for production model evaluation.
• Understanding of data quality frameworks, schema validation, and automated testing for data pipelines.
• Familiarity with data governance principles, data lineage, and compliance requirements.

Project & Delivery Management:

• Ability to manage multiple concurrent projects, prioritise effectively based on business impact, and deliver results under tight timelines.
• Strong problem-solving capabilities with structured approaches to breaking down complex challenges.

Nice to have:

• Demonstrated experience building analytics solutions in FMCG, CPG, retail, or beverage alcohol industries with measurable business impact.
• Experience with causal inference methods (difference-in-differences, propensity score matching, synthetic controls) for measuring promotional effectiveness and marketing mix modelling.
• Experience with advanced forecasting techniques such as hierarchical forecasting or neural forecasting methods.
• Familiarity with LLMs and generative AI applications in business contexts.
• Experience building consumer segmentation, churn prediction, or customer lifetime value models.
• Certifications in cloud platforms (Azure Data Scientist Associate, AWS ML Specialty) or Databricks certifications.
• Experience working in agile environments using frameworks like Scrum or Kanban, including sprint planning, backlog grooming, and iterative delivery.

Closing Date: 31 January 2026