Global ML Engineering

Campari Group today is a major player in the global branded spirits industry, with a portfolio of over 50 premium and super premium brands, marketed and distributed in over 190 markets around the world, with leading positions in Europe and the Americas.

Headquartered in Milan, Italy, Campari Group owns 25 plants worldwide and has its own distribution network in 26 countries, and employs approximately 4,700 people.

Shares of the parent company Davide Campari - Milano N.V. are listed on the Italian Stock Exchange since 2001. Campari Group is today the sixth-largest player worldwide in the premium spirits industry.

Mission The Global Machine Learning Engineer is responsible for accelerating the delivery, industrialisation and scaling of Machine Learning capabilities across Campari Group, with a particular focus on Revenue Growth Management, forecasting, pricing optimisation, promotional effectiveness and commercial analytics. The role transforms advanced analytical prototypes into robust, reusable and production-ready AI products that improve decision quality, increase automation, reduce external dependency and unlock measurable revenue, margin and operational efficiency benefits.

General Description of the Role Within the Technology & Services organization, the AI, Data & Analytics team, the Global Machine Learning Engineer is responsible for enabling data-driven decision making and accelerating business value through data, analytics and Artificial Intelligence capabilities.

The Global Machine Learning Engineer plays a critical role in designing, developing, deploying and maintaining machine learning models, optimisation engines and production-grade AI solutions that support strategic initiatives across Revenue Growth Management, demand forecasting, pricing optimisation, promotion effectiveness, sales planning and commercial decision-making.

The role bridges data science, data engineering, business stakeholders and technology platforms, ensuring that AI and ML models move from proof-of-concept into robust, secure, monitored and reusable products that can be deployed and adopted at global scale. The Machine Learning Engineer will contribute to MLOps practices, automated model retraining, model monitoring, explainability and governance, enabling sustainable adoption across markets and brands.

Key Responsibilities and Activities Machine Learning Model Development Design, develop, validate and maintain machine learning models supporting forecasting, pricing optimisation, promotion optimisation and commercial analytics use cases.

Develop scalable forecasting models across demand, sales and commercial planning processes, supporting improved business planning, S&OP effectiveness and decision quality.

Build optimisation engines and analytical models that support Revenue Growth Management decisions, including trade investment, promotional effectiveness and net sales performance opportunities.

Translate business needs into robust ML technical solutions, balancing accuracy, interpretability, usability and operational feasibility.

MLOps, Industrialisation & Productisation Transform analytical prototypes and data science models into scalable, production-ready AI products that can be deployed, monitored and maintained globally.

Design and implement MLOps pipelines for automated model training, retraining, deployment, versioning, performance monitoring and lifecycle management.

Establish reusable model components, technical patterns and deployment accelerators that can be replicated across markets, brands and business functions.

Ensure production ML solutions are reliable, maintainable and aligned with enterprise architecture, security and operational standards.

RGM (Revenue Growth Management) & Forecasting Enablement Support the industrialisation of AI-enabled RGM use cases, including price optimisation, promotion optimisation, trade investment decision support and predictive commercial insights.

Improve forecast accuracy by embedding advanced ML models in commercial and planning workflows.

Enable predictive and prescriptive analytics capabilities that move KPI usage beyond retrospective reporting and towards forward-looking decision support.

Collaborate with business teams to ensure ML solutions are adopted and embedded into relevant planning, commercial and performance management processesAI Governance, Explainability & Model Monitoring Support model explainability, transparency and governance requirements, ensuring business stakeholders can understand and trust model outputs.

Monitor model quality, drift, performance and adoption, recommending improvements and corrective actions when required.

Collaborate with Data Governance, Security, Enterprise Architecture and business stakeholders to ensure ML solutions comply with company standards and responsible AI principles.

Maintain documentation, controls and operating practices required for production-grade ML solutions.

Enterprise Integration & Automation Integrate ML outputs into enterprise…