{"id":3755880,"url":"https://alion.io/job/mondelez-international-data-scientist","title":"Data Scientist","company":{"id":13897,"name":"Mondelez International","domain":"mondelezinternational.com","url":"https://alion.io/company/mondelezinternational","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":129,"ghost_share":0,"stale_share":0.659,"repost_share":0.008,"time_to_fill_p50_days":22,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CAPA","optional":false},{"name":"CI/CD","optional":false},{"name":"Collibra","optional":false},{"name":"Computer Vision","optional":false},{"name":"Databricks","optional":false},{"name":"Design of Experiments","optional":false},{"name":"FMEA","optional":false},{"name":"GitHub","optional":false},{"name":"Grafana","optional":false},{"name":"LIMS","optional":false},{"name":"Machine Learning","optional":false},{"name":"MuleSoft","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SAP BTP","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Statistical Process Control","optional":false},{"name":"Tableau","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"ABAP","optional":true},{"name":"Apex","optional":true},{"name":"Data Vault","optional":true},{"name":"Embeddings","optional":true},{"name":"Feature Store","optional":true},{"name":"GMP","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"MLFlow","optional":true},{"name":"RAG","optional":true},{"name":"Semantic Search","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-03T00:00:00Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-06T02:23:53Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Job Description\nAre You Ready to Make It Happen at Mondelēz International?\nJoin our mission to change the way the world snacks. Make It With Pride.\nYou 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.\nHow you will contribute\nYou will:\nAnalyze 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\nAdvise on the details of data structures and associated components\nManage the iteration, review and maintenance of data requirements and data models and you will assist in creating the sematic layer of data\nHave cross-functional understanding to create reusable data assets\nChoose 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\nReconcile 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 \nRationalize the relationships expressed in the logical data model to the schema of the physical data store (that is, database tables and columns)\nWhat you will bring\nA desire to drive your future and accelerate your career and the following experience and knowledge:\nExtensive experience in data modeling in a large complex business with multiple systems\nAbility to simplify complex problems and communicate them to a broad audience\nMore about this role\nAt 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.\nYou 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.\nYour 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.\nWhat you need to know about this position:\nQuality Control Management System (QCMS) - Primary Focus\nBuild 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.\nDevelop 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.\nCreate 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.\nBuild 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).\nDevelop calibration and measurement-system analytics for calibration overdue risk, instrument drift, measurement uncertainty, compliance gaps, and quality impact from calibration failures.\nUse 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.\nBuild 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.\nBuild quality dashboards, risk scorecards, alerts, and self-service analytics for Q&FS, BU Quality, Global Food Safety, Plant Operations, and RDQ stakeholders.\nManufacturing Data Science - Core / Supporting Focus\nBuild IIoT-enabled analytics using data from PLCs, SCADA, historians, MES , sensors, edge devices, vision systems, LIMS, and QMS platforms.\nDevelop 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.\nSupport automated process control and closed-loop control through process modelling, deviation prediction, setpoint recommendations, and feedback-driven control strategies.\nApply 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.\nBuild mathematical optimisation and simulation models for production scheduling, line balancing, capacity planning, material flow, quality-resource allocation, sanitation scheduling, and calibration planning.\nSupport Deployment & Industrialization of models to cloud, edge, or on-premise environments with monitoring, retraining, governance, and auditability.\nPlatform, Data & Delivery\nPartner 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.\nDesign, build, validate, deploy, and continuously improve ML, statistical, NLP, computer-vision, optimisation, simulation, and GenAI solutions using Databricks and Azure/AWS .\nDesign 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.\nIntegrate 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.\nBuild 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.\nOperationalise models through MLOps practices, including version control (GitHub/ADO), CI/CD, model monitoring, drift detection, retraining, governance, security, and auditability.\nQuantify business impact through MDLZ-specific quality, food-safety, manufacturing, and supply-chain KPIs.\nWhat extra ingredients you will bring:\nMandatory\n5-8+ years of experience in quality analytics, food-safety analytics, manufacturing analytics, supply-chain analytics, industrial AI, or data science in an operational/FMCG environment.\n 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.\nStrong Python skills, including pandas, NumPy, scikit-learn, time-series libraries, and TensorFlow or PyTorch.\nStrong SQL, PySpark, and experience building scalable data pipelines and governed data products in Databricks.\nStrong foundation in machine learning, statistics, time-series analysis, anomaly detection, classification, regression, forecasting, and model validation.\nExperience designing end-to-end data-science and data-engineering architectures on cloud, including ingestion, storage, processing, feature engineering, model training, deployment, monitoring, and governance.\nExperience building dashboards using Tableau, Power BI, Grafana, Databricks SQL, or Python-based tools.\nExperience working with QMS/eQMS, IMS, MES, SAP ERP, CMMS, historians, IIoT platforms, or audit-management systems.\nStrong stakeholder-management, communication, and data-storytelling skills - comfortable presenting to both plant-floor teams and senior ISC leadership.\nGood to Have\nKnowledge of quality and food-safety frameworks such as HACCP, GMP, PRPs, Hygienic Zoning, allergen control, environmental monitoring, sanitation controls, and CAPA - familiarity with MDLZ Quality Policies (QPs) is a plus.\nKnowledge of Manufacturing & Domain Frameworks such as Industry 4.0, IoT telemetry, Data Vault 2.0, and statistical process control (SPC) is a plus.\nExperience with NLP techniques such as text classification, named-entity recognition, topic modelling, embeddings, semantic search, and retrieval-augmented generation.\nExperience with computer vision, image processing, object detection, segmentation, and defect classification in manufacturing or food-production environments.\nExperience deploying GenAI solutions using LLMs, vector databases, prompt engineering, and responsible-AI guardrails.\nFamiliarity with optimisation tools such as PuLP, Gurobi, CPLEX, OR-Tools, or simulation platforms.\nExposure to IL6S (Integrated Lean Six Sigma), SPC, CpK/PpK, MSA, 8D, 5-Why, FMEA, and structured root-cause analysis.\nExperience with MLOps, MLflow, model registries, data drifts, feature stores, streaming analytics, edge deployment, and Collibra -based or Ataccama -based data governance.\nUnderstanding of manufacturing and supply-chain systems including SAP S/4HANA, SAP ECC, SAP EWM, o9 (IBP/S&OE), MES , CMMS, APS, TMS, LIMS, QMS/eQMS, IOT, Connected Worker and industrial historians.\nEducation / Certifications:\nBachelor'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.\nMaster's degree or advanced qualification in Data Science, AI, Industrial Engineering, Quality Engineering, Food Safety, or Operations Research is preferred.\nCertifications:\nDatabricks Data Engineer, Machine Learning, or Generative AI Engineer certification.\nAzure Data Scientist / Engineer Associate, or AWS Certified Machine Learning - Specialty / Data Engineer - Associate / Solutions Architect or equivalent.\nIL6S / Lean Six Sigma Green Belt or Black Belt.\nHACCP, PCQI, FSSC 22000, ISO 22000, or BRCGS certification is advantageous.\nJob specific requirements:\nQuality & Food Safety\nImproved QCMS audit scores and FSSC 22000 audit readiness.\nReduced CAPA closure time, improved CAPA effectiveness, and reduction in repeat findings.\nReduced consumer complaints, non-conformances, quality escapes, and quality-related holds.\nReduced food-safety risks through sanitation, pathogen-monitoring, hygienic zoning, and environmental-monitoring analytics.\nManufacturing\nReduced RM/PM Yield Loss (%) - raw material and packaging material losses against zero-base cost.\nImproved OEE (Overall Equipment Effectiveness), throughput, and schedule adherence.\nReduce...","description_format":"text","description_chars":14438,"description_truncated":true,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Snack Food","Confectionery"],"lifecycle":[{"event":"open","at":"2026-11-04T16:52:51Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":3,"expected_fill_days":22,"reasons":["conf:3","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-11-06T05:58:05Z"},"pay":null,"html_url":"https://alion.io/job/mondelez-international-data-scientist","json_url":"https://alion.io/job/mondelez-international-data-scientist.json","meta":{"generated_at":"2026-11-06T08:08:16Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":14570,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-07T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":13897},"rest":"https://alion.io/mcp/rest/get_company?id=13897"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmondelez-international-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fmondelez-international-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fmondelez-international-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/mondelez-international-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmondelez-international-data-scientist"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3755880}},{"tool":"search_jobs","arguments":{"company":"mondelezinternational.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3755880},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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