Data Scientist at The Heineken Company

• Develop and deploy production-grade machine learning and artificial intelligence solutions that generate measurable business value across the organisation.
• The Data Scientist contributes to end-to-end analytics delivery, building scalable solutions in collaboration with team members and business stakeholders.
• This role combines solid technical skills in ML development and MLOps fundamentals with effective communication and a drive for continuous learning.

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
• Participate in code applying team standards for quality, maintainability and best practices to continuously improve personal and team output.
• Share knowledge and collaborate with team members on technical approaches the data science domain.
• 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 relationships with regional and global analytics, data and business stakeholders to facilitate cross-functional collaboration.

Qualifications, Experience amd skills required per job levels:

Qualifications:

• Bachelors Degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, Physics, or related quantitative field required.
• Master’s degree preferred.
• 2-5years 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.
• Stays current with machine learning and artificial intelligence industry trends and emerging technologies, evaluating applicability to business problems and sharing knowledge with the team.

Leadership competencies 

• Fosters a growth mindset and culture of continuous learning within the analytics team.
• Builds strong credibility with analytics and business stakeholders through consistent delivery of high-quality solutions.
• Promotes a culture of collaboration in the team and between teams.
• Balances multiple priorities effectively, making trade-off decisions that optimise business impact within resource constraints.
• Demonstrates composure under pressure when managing tight deadlines, changing requirements, or technical challenges.
• Learns from setbacks and incorporates lessons into improved approaches for future projects.

Closing Date: 23/04/2026