Data Scientist

Job Title: Data Scientist Company: Mondelēz International Job Category: SAP EWM Roles & Responsibilities: Job Description ------------------- 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. * 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) 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. ### 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 Excellence teams. * Build mathematical optimisation and simulation models for production scheduling, line balancing, capacity planning, material flow, quality\-resource allocation, sanitation scheduling, and calibration planning. * Support Deployment \& Industrialization of models to cloud, edge, or on\-premise environments with monitoring, retraining, governance, and auditability. * Partner with Q\&FS, BU Quality, Global Food Safety, RDQ, Manufacturing, Supply Chain Excellence, CS\&L, Process Excellence, Engineering/Maintenance, Procurement, HSE, and MDS (Mondelēz Digital Services) teams to define high\-impact use cases and measurable success criteria. * Design, build, validate, deploy, and continuously improve ML, statistical, NLP, computer\-vision, optimisation, simulation, and GenAI solutions using Databricks and Azure/AWS . * Design and implement scalable, secure, and governed data architectures and data\-science solution patterns on Azure/AWS in collaboration with the AI, Data \& Analytics Platform team. * Integrate data from SAP S/4HANA, SAP ECC, MES, SAP EWM, o9, IMS, QMS/eQMS (QCMS), food\-safety systems, audit tools, and IIoT platforms into reliable Databricks\-based data products — leveraging Collibra / Ataccama for data governance and MuleSoft / SAP BTP for integration. * Build real\-time dashboards, alerts, scorecards, and self\-service analytics using Tableau, Power BI, Databricks SQL, or Python\-based tools for Q\&FS, plant, and ISC stakeholders — feeding into Executive dashboards and KPI reporting where applicable. * Operationalise models through MLOps practices, including version control (GitHub/ADO), CI/CD, model monitoring, drift detection, retraining, governance, security, and auditability. * Quantify business impact through MDLZ\-specific quality, food\-safety, manufacturing, and supply\-chain KPIs. * 5–8\+ years of experience in quality analytics, food\-safety analytics, manufacturing analytics, supply\-chain analytics, industrial AI, or data science in an operational/FMCG environment. * Hands\-on, production\-level experience with Databricks and Strong cloud experience (Azure and/or AWS) is required, particularly across data engineering, data\-science solutioning, and solution architecture. * Strong Python skills, including pandas, NumPy, scikit\-learn, time\-series libraries, and TensorFlow or PyTorch. * Strong SQL, PySpark, and experience building scalable data pipelines and governed data products in Databricks. * Strong foundation in machine learning, statistics, time\-series analysis, anomaly detection, classification, regression, forecasting, and model validation. * Experience designing end\-to\-end data\-science and data\-engineering architectures on cloud, including ingestion, storage, processing, feature engineering, model training, deployment, monitoring, and governance. * Experience building dashboards using Tableau, Power BI, Grafana, Databricks SQL, or Python\-based tools. * Experience working with QMS/eQMS, IMS, MES, SAP ERP, CMMS, historians, IIoT platforms, or audit\-management systems. * Strong stakeholder\-management, communication, and data\-storytelling skills — comfortable presenting to both plant\-floor teams and senior ISC leadership. * Improved QCMS audit scores and FSSC 22000 audit readiness. * Reduced CAPA closure time, improved CAPA effectiveness, and reduction in repeat findings. * Reduced consumer complaints, non\-conformances, quality escapes, and quality\-related holds. * Reduced food\-safety risks through sanitation, pathogen\-monitoring, hygienic zoning, and environmental\-monitoring analytics. * Improved CSL (Customer Service Level), Supply Reliability, and Demand Supportability. * Improved SFA (Statistical Forecast Accuracy) and reduced forecast bias where analytics support demand\-sensing. * Improved inventory turns, safety\-stock optimisation, and supplier\-quality performance. * Reduced COGS impact through optimised production scheduling and capacity utilisation. * Number of production\-deployed AI/ML solutions, adoption rate, model reliability, and quantified business value ($ impact to Conversion P\&L and ISC cost). * Active engagement on the MARCO dashboards and KPI reporting framework. * Stakeholder satisfaction and self\-service adoption across Q\&FS, Manufacturing, and ISC functions. Within Country Relocation support available and for candidates voluntarily moving internationally some minimal support is offered through our Volunteer International Transfer Policy Business Unit Summary ------------------------- Work Location: Maharashtra, India Availability: Full time

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 Education / Certifications: * Bachelor's or Master's degree in Data Science, Computer Science, Industrial Engineering, Chemical/Mechanical/Electrical Engineering, Food Science, Statistics, Operations Research, or a related field. * Master's degree or advanced qualification in Data Science, AI, Industrial Engineering, Quality Engineering, Food Safety, or Operations Research is preferred. Certifications: * Databricks Data Engineer, Machine Learning, or Generative AI Engineer certification. * Azure Data Scientist / Engineer Associate, or AWS Certified Machine Learning – Specialty / Data Engineer – Associate / Solutions Architect or equivalent. * IL6S / Lean Six Sigma Green Belt or Black Belt. * HACCP, PCQI, FSSC 22000, ISO 22000, or BRCGS certification is advantageous. Job specific requirements: Key Skills: SAP EWM Salary: Not disclosed