ppCampari 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. /p pHeadquartered in Milan, Italy, Campari Group owns b25 /b plants worldwide and has its own distribution network in b26 /b countries, and employs approximately b4,700 /b people. /p pShares 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. /p h3Mission /h3 pThe 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. /p h3General Description Of The Role /h3 pWithin 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. /p pThe 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. /p pThe 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. /p h3Key Responsibilities And Activities /h3 h3Machine Learning Model Development /h3 ul liDesign, develop, validate and maintain machine learning models supporting forecasting, pricing optimisation, promotion optimisation and commercial analytics use cases. /li liDevelop scalable forecasting models across demand, sales and commercial planning processes, supporting improved business planning, SOP effectiveness and decision quality. /li liBuild optimisation engines and analytical models that support Revenue Growth Management decisions, including trade investment, promotional effectiveness and net sales performance opportunities. /li liTranslate business needs into robust ML technical solutions, balancing accuracy, interpretability, usability and operational feasibility. /li /ul h3MLOps, Industrialisation Productisation /h3 ul liTransform analytical prototypes and data science models into scalable, production-ready AI products that can be deployed, monitored and maintained globally. /li liDesign and implement MLOps pipelines for automated model training, retraining, deployment, versioning, performance monitoring and lifecycle management. /li liEstablish reusable model components, technical patterns and deployment accelerators that can be replicated across markets, brands and business functions. /li liEnsure production ML solutions are reliable, maintainable and aligned with enterprise architecture, security and operational standards. /li /ul h3RGM (Revenue Growth Management) Forecasting Enablement /h3 ul liSupport the industrialisation of AI-enabled RGM use cases, including price optimisation, promotion optimisation,



trade investment decision support and predictive commercial insights. /li liImprove forecast accuracy by embedding advanced ML models in commercial and planning workflows. /li liEnable predictive and prescriptive analytics capabilities that move KPI usage beyond retrospective reporting and towards forward-looking decision support. /li liCollaborate with business teams to ensure ML solutions are adopted and embedded into relevant planning, commercial and performance management processes /li /ul h3AI Governance, Explainability Model Monitoring /h3 ul liSupport model explainability, transparency and governance requirements, ensuring business stakeholders can understand and trust model outputs. /li liMonitor model quality, drift, performance and adoption, recommending improvements and corrective actions when required. /li liCollaborate with Data Governance, Security, Enterprise Architecture and business stakeholders to ensure ML solutions comply with company standards and responsible AI principles. /li liMaintain documentation, controls and operating practices required for production-grade ML solutions. /li /ul h3Enterprise Integration Automation /h3 ul liIntegrate ML outputs into enterprise platforms, business workflows, dashboards and decision-support tools. /li liCollaborate with Data Platform, Data Engineering and Application teams to ensure the availability, quality and scalability of the data pipelines required by ML products. /li liSupport automation of repetitive analytical activities, enabling business teams to focus on higher-value interpretation, planning and decision-making. /li liContribute to Agentic AI scenarios where forecasting, optimisation and business workflows are connected through intelligent assistants and agents. /li /ul h3Stakeholder Partnership Continuous Improvement /h3 ul liPartner with business stakeholders to prioritise ML use cases based on value, feasibility, scalability and strategic relevance. /li liProvide technical guidance to data scientists, analysts and business teams on ML engineering, deployment and maintainability considerations. /li liStay informed about emerging machine learning, optimisation and MLOps technologies, assessing relevance for Campari Group priorities. /li liContribute to building sustainable internal AI capabilities and reducing long-term dependency on external consultants and contractors. /li /ul h3Required Skills And Experience /h3 h3Experience Background /h3 ul li7+ years of experience in Machine Learning Engineering, Data Science, Data Engineering, Software Engineering or similar AI engineering roles. /li liProven experience developing, deploying and maintaining machine learning models in production environments. /li liExperience with forecasting, optimisation, predictive modelling or commercial analytics use cases is strongly preferred. /li liExperience operating in complex, international business environments and working with cross-functional stakeholders. /li liExperience translating analytical prototypes into scalable products and reusable capabilities. /li /ul h3Machine Learning Forecasting Skills /h3 ul liStrong knowledge of supervised and unsupervised machine learning techniques, time-series forecasting, optimisation methods and model evaluation approaches. /li liExperience building demand forecasting, sales forecasting, pricing, promotion optimisation or decision-support models. /li liUnderstanding of model explainability, feature engineering, model monitoring and model performance management. /li liAbility to balance model accuracy with business interpretability, scalability and operational adoption.



/li liKnowledge of AI-enabled decision-support capabilities in commercial, planning or supply chain contexts is considered a plus. /li /ul h3Technical MLOps Skills /h3 ul liStrong programming skills in Python and familiarity with common ML libraries and frameworks. /li liHands-on experience with cloud-based AI and data platforms, preferably Azure AI Services, Azure Machine Learning, Databricks or equivalent technologies. /li liExperience designing MLOps pipelines, CI/CD workflows, model registries, automated retraining, model monitoring and deployment automation. /li liExperience working with APIs, data pipelines, version control, containerisation and modern software engineering practices. /li liUnderstanding of data governance, cybersecurity, enterprise architecture and responsible AI principles. /li /ul h3Business Skills /h3 ul liAbility to translate business challenges into practical machine learning solutions and clearly communicate model outputs to non-technical audiences. /li liStrong analytical, problem-solving and prioritisation skills, with a focus on measurable business value. /li liAbility to work closely with Commercial, RGM, Supply Chain, Finance, IT and Data Analytics stakeholders. /li liStrong stakeholder management, collaboration and influencing skills. /li /ul h3Education /h3 ul liBachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Statistics, Mathematics or related fields. /li liRelevant certifications in Machine Learning, Cloud, Data Engineering or MLOps are considered a plus. /li /ul h3Key Competencies /h3 ul liStrong analytical mindset and passion for machine learning, optimisation and business value creation. /li liAbility to combine technical excellence with pragmatic delivery and adoption focus. /li liStructured problem-solving approach and strong attention to quality, reliability and scalability. /li liAccountability and ownership mindset, with the ability to move solutions from prototype to production. /li liAbility to operate effectively in ambiguous, fast-evolving and cross-functional environments. /li liStrong collaboration and stakeholder management across global teams and business functions. /li liCuriosity and continuous learning attitude towards new ML, AI and MLOps technologies. /li liResults-oriented approach focused on measurable improvements in revenue, margin, productivity and operational efficiency. /li liStrong communication skills and ability to explain technical concepts and model outcomes to business stakeholders. /li liFluent English. /li /ul pThe indicative annual gross salary (base salary) for this position ranges within 55K-70K + 10% STI, depending on the candidate’s experience, skills, and overall profile. /p pThe role is classified in accordance with the collective labor agreement – level 1 /p pThe position might also include a variable compensation component and a benefits package in line with applicable company policies /p h3Our commitment to Diversity Inclusion /h3 pAt Campari Group we believe in building more value together, thus we see diversity in all forms as a source of enrichment. Our employment policies and practices ensure that we are committed to providing equal employment opportunities in all aspects of employment without regard to any individual’s race, religion, creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, sexual orientation, gender identity or characteristics or expression, political affiliation or activity, age, veteran status, citizenship, or any other characteristic protected by law. /p pCampari Group believes that fair compensation and equal opportunities are crucial for employees’ well‑being, empowerment, and engagement. Our efforts to ensure fair pay have earned us the Fair Pay Certification by Fair Pay Workplace, an independent organization dedicated to dismantling pay disparities based on gender, race and their intersection. /p /p #J-18808-Ljbffr

📌 Global ML Engineering (Sesto San Giovanni)
🏢 Campari Group
📍 Sesto San Giovanni