Data Scientist

Job Description

Are You Ready to Make It Happen at Mondelēz International?

Join our mission to change the way the world snacks. Make It With Pride.

You will represent and communicate data requirements to help us understand our data assets and the relationships among them. You will investigate, analyze and scope data requirements to support the development of data integration, data retrieval and reusable data sets.

How you will contribute

You will

Analyze business requirements as a guide for data modeling and apply data analysis, design, modeling, and quality assurance techniques, based on a detailed understanding of business processes, to establish, modify or maintain data structures and associated components (entity descriptions, relationship descriptions, attribute definitions

Advise on the details of data structures and associated components

Manage the iteration, review and maintenance of data requirements and data models and you will assist in creating the sematic layer of data

Have cross-functional understanding to create reusable data assets

Choose a suitable data modeling approach for each project, such as by assessing the suitability of existing data models and build data models with the flexibility to change when business requirements change

Reconcile multiple logical source models into a single, logically consistent model and you will ensure that the proposed model follows data architecture guidelines and best practices

Rationalize the relationships expressed in the logical data model to the schema of the physical data store (that is, database tables and columns)

What you will bring

A desire to drive your future and accelerate your career and the following experience and knowledge

Extensive experience in data modeling in a large complex business with multiple systems

Ability to simplify complex problems and communicate them to a broad audience

More about this role

At Mondelēz International, data science is a critical accelerator in our mission to lead the future of snacking. We are looking for a Data Scientist to join our ISC Central Analytics team and strengthen the data-science capability across our Integrated Supply Chain — with a primary focus on Quality and Food Safety analytics and support / scale our Manufacturing Data Science practice.

You will sit at the intersection of quality, food safety, and manufacturing operations, building AI, machine-learning, NLP, GenAI, computer-vision, optimisation, and simulation solutions that make our supply chain smarter, safer, and more resilient. The quality dimension is your home base — QCMS performance, audit readiness, QFS intelligence, compliance risk — but you will also extend the Central Analytics team's manufacturing data-science portfolio by helping scale predictive maintenance, process optimisation, and IIoT-driven analytics across the plant network.

Your work will transform quality, food-safety, and manufacturing data into production-ready intelligence that reduces Quality & Food Safety defects, non-conformances, compliance risk, RM/PM yield loss, unplanned downtime, and cost — while improving throughput, Customer Service Level (CSL), asset reliability, and operational efficiency.

What you need to know about this position

Quality Control Management System (QCMS) — Primary Focus

Build quality prediction models for product-quality outcomes (CTQ and CTP parameters), supplier quality risk, defect probability, non-conformance risk, process drift, and first-pass yield across Biscuits, Chocolate, Gum & Candy categories.

Develop QCMS audit-score prediction models to anticipate internal and audit outcomes, identify likely findings, quantify site-level quality risk, and prioritise preventive actions ahead of audits.

Create Quality Intelligence using NLP, clustering, causal analysis, and machine learning to classify non-conformances, identify recurring root causes, predict recurrence, score effectiveness, and predict closure delays — aligned with Mondelēz CAPA and Quality Policy (QP) frameworks.

Build analytics for hazards, hygienic zoning, food safety, pathogen monitoring, environmental monitoring, sanitation, allergen control, and cross-contamination risk in line with the Mondelēz HACCP Standard, Hygienic Zoning Programmes, and Prerequisite Programmes (PRPs).

Develop calibration and measurement-system analytics for calibration overdue risk, instrument drift, measurement uncertainty, compliance gaps, and quality impact from calibration failures.

Use NLP and GenAI to extract insights from QCMS audit reports, CAPA narratives, consumer complaints, deviations, SOPs, HACCP/Food Safety Plans (FSP), maintenance logs, and supplier communications; enable AI-assisted quality investigations and audit preparation.

Build analytics connecting supplier quality, incoming-material risk, and customer-quality outcomes to create end-to-end quality visibility across the supply chain — from raw material to consumer.

Build quality dashboards, risk scorecards, alerts, and self-service analytics for Q&FS, BU Quality, Global Food Safety, Plant Operations, and RDQ stakeholders.

Manufacturing Data Science — Core / Supporting Focus

Build IIoT-enabled analytics using data from PLCs, SCADA, historians, MES , sensors, edge devices, vision systems, LIMS, and QMS platforms.

Develop predictive-maintenance models for equipment failure, asset health, remaining useful life, condition-based monitoring, anomaly detection, and maintenance prioritisation — leveraging IL6S (Integrated Lean Six Sigma) loss-elimination methodology.

Support automated process control and closed-loop control through process modelling, deviation prediction, setpoint recommendations, and feedback-driven control strategies.

Apply statistical process control, process-capability analysis (CpK/PpK), design of experiments, and root-cause methods (8D, 5-Why, FMEA) to reduce variation and improve yield — in partnership with the Process Excellence and Supply Chain Excelle