Senior Data Scientist

Senior Data Scientist

About the Global Analytics Team

The mission of Global Analytics is to lead HEINEKEN into becoming a data-driven company and the best-connected brewer. As a team, we foster a data-driven entrepreneurial culture across the company. We act as an incubator for smart data products in all business areas from sales to logistics, marketing, and purchasing rapidly launching value-creating use cases, such as optimized spare parts management and smarter media spending. This year, our focus is to scale these and other use cases to as many countries as possible around the globe.

Our team comprises data scientists, engineers, business intelligence specialists, and analytics translators, working together to deliver data-driven solutions across the business. We operate across three continents, with satellite teams in South Africa, Poland, India, and Singapore. We are collaborative, innovative, and reliable, embracing diverse cultures and perspectives. We strive to create an environment where team members enjoy both the challenges they tackle and the people, they solve them with.

We innovate to transform HEINEKEN from a traditional to a data-driven company. We position ourselves as a business partner, driving value-creating decisions and building trust in our solutions. If these challenges sound interesting and exciting, we invite you to apply. We have ambitious goals, and we need your help to achieve them.

Position Overview

Senior Data Scientist

Key Responsibilities

• End-to-End Model Development:

  Design, implement, and maintain full-stack data science workflows. This includes data ingestion (ETL/ELT), rigorous data preprocessing and feature engineering, model training, validation, and deployment into production. You will refactor and harden data preprocessing pipelines to improve stability and robustness, ensuring reliable data quality and consistency over time.

• Advanced Analytics & Machine Learning:

  Develop and optimize predictive models (regression, classification, forecasting, etc.) using state-of-the-art techniques. Continuously improve model accuracy by implementing advanced evaluation metrics such as weighted R², adjusted R², correlation coefficients, RMSE, and MAE. Enhance feature selection processes and hyperparameter optimization routines to extract maximum value from data.

• Model Stability & Validation:

  Perform comprehensive model stability testing and validation. Use fixed random seeds for reproducibility, bootstrap resampling to assess data variability, and temporal hold-out windows to evaluate model decay over time. Analyse feature stability and hyperparameter sensitivity to ensure model robustness across different customer segments, brands, packaging, or markets. Automate the baseline-model validation process to establish repeatable benchmarks and speed up iteration on new models.

• Productionization & Automation:

  Productionize helper functions and data pipelines. This includes automating calculations (e.g. conversion/redemption rates) and standardizing feature computation so models can be retrained quickly and reliably. Collaborate with data engineers to implement scalable pipelines on Azure (Data Factory, Data Lake, Databricks) and deploy models via CI/CD processes (e.g. using Azure DevOps or similar tools). Ensure solutions adhere to secure coding practices and data governance standards.

• Technical Leadership (Individual Contributor):

  Act as a subject-matter expert and individual contributor on data science best practices. Write clean, maintainable code in Python (following OOP principles and design patterns) and document workflows in Jupyter Notebooks or VS Code. Stay current with emerging tools and techniques to continuously improve our data infrastructure and methodologies.

• Communication & Visualization:

  Translate technical results into clear business insights. Create dashboards or reports (e.g. using Power BI, Streamlit, or similar) to present findings to non-technical stakeholders. Provide concise documentation and explanation of model assumptions, limitations, and impacts on business decisions.

Required Qualifications and Skills

• Education & Experience:

  MS or PhD in Computer Science, Statistics, Mathematics, or a related field. At least 8+ years of experience (or equivalent proven track record) building and deploying data science and machine learning solutions in a business setting

• Programming & Technical Skills:

  Expert proficiency in Python (including OOP and design patterns) for data science. Comfortable coding in Jupyter Notebook and VS Code. Hands-on experience with ML libraries (scikit-learn, TensorFlow, PyTorch) and data libraries (pandas, NumPy). Familiarity with big data tools (Spark, Dask, etc.) and version control (Git).

• Cloud & Deployment:

  Demonstrated expertise with Azure cloud services for end-to-end data solutions. This includes Azure Machine Learning (for model development and deployment), Azure Data Factory (ETL/ELT pipelines), Azure Databricks (data processing), and related services. Ability to architect scalable data pipelines and CI/CD processes in Azure is crucial. Experience with containerization (Docker) and orchestration (Kubernetes) is a plus.

• Statistical & Analytical Skills:

  Deep understanding of statistical modelling, experimental design, and machine learning. Ability to select and apply appropriate evaluation metrics and rigorously validate models to ensure high predictive performance. Familiarity with advanced techniques (e.g. Bayesian methods, time series analysis) is beneficial.

• Business Acumen & Communication:

  Strong business sense to translate analytical outputs into actionable strategies. Proven ability to collaborate with stakeholders and clearly communicate complex technical concepts to non-technical audiences.

• Soft Skills:

  Highly organized, proactive problem-solver who thrives in a fast-paced environment. Self-motivated individual contributor with a continuous learning mindset. Attention to detail and commitment to quality in all deliverables.

Why Join Us

• Impact:

  Work on mission-critical projects where your models directly influence key business decisions and drive value.

• Innovation:

  Leverage cutting-edge tools and frameworks in an environment that encourages experimentation and learning.

• Growth:

  Collaborate with a multidisciplinary team of experts; receive support and resources to advance your career as a data science leader.

• Culture:

  Join a collaborative, inclusive team that values shared knowledge, clear communication, and strong work–life balance.

If you are passionate about building robust, production-quality data science solutions and have the depth of expertise to drive projects from data to deployment, we want to hear from you. Apply now to become our Senior Data Scientist and help propel our organization toward data-driven excellence!