Data Scientist

Accountabilities: In this role, you will design, develop, and operationalize data science solutions that improve quality, food safety, manufacturing efficiency, and supply chain performance. Your responsibilities will include: Develop predictive models for product quality outcomes, critical quality parameters, supplier risks, defect probabilities, non-conformance risks, process deviations, and first-pass yield across multiple product categories. Build audit-score prediction models to anticipate audit outcomes, identify potential findings, assess site-level quality risks, and prioritize preventive actions. Apply NLP, clustering, causal analysis, and machine learning to classify non-conformances, identify recurring root causes, predict recurrence, and evaluate corrective and preventive action (CAPA) effectiveness. Develop analytics for food safety hazards, hygienic zoning, pathogen monitoring, environmental monitoring, sanitation, allergen control, and cross-contamination risks. Create calibration and measurement-system analytics to identify overdue calibration risks, instrument drift, measurement uncertainty, and compliance gaps. Use NLP and Generative AI to extract insights from audit reports, CAPA narratives, consumer complaints, standard operating procedures, food safety plans, maintenance logs, and supplier communications. Build end-to-end quality analytics connecting supplier performance, incoming material risks, and customer quality outcomes. Develop dashboards, risk scorecards, alerts, and self-service analytics for Quality, Food Safety, Manufacturing, and Supply Chain stakeholders. Build Industrial Internet of Things (IIoT)-enabled analytics using data from PLCs, SCADA systems, historians, MES platforms, sensors, edge devices, vision systems, LIMS, and quality management platforms. Develop predictive maintenance models covering equipment failures, asset health, remaining useful life, anomaly detection, and maintenance prioritization. Support automated process control through process modeling, deviation prediction, setpoint recommendations, and feedback-driven control strategies. Apply statistical process control, process capability analysis (CpK/PpK), design of experiments, and structured root-cause methodologies such as 8D, 5-Why, and FMEA. Develop optimization and simulation models for production scheduling, line balancing, capacity planning, material flows, quality resource allocation, sanitation scheduling, and calibration planning. Support the deployment and industrialization of data science models across cloud, edge, and on-premise environments, ensuring monitoring, retraining, governance, and auditability. Collaborate with Quality and Food Safety, Manufacturing, Research and Development, Engineering, Maintenance, Procurement, Supply Chain Excellence, and Digital Services teams to identify high-impact use cases and define measurable success criteria. Design, develop, validate, deploy, and continuously improve machine learning, statistical, NLP, computer vision, optimization, simulation, and Generative AI solutions using Databricks and Azure and/or AWS. Build scalable, secure, and governed cloud data architectures in collaboration with AI, Data, and Analytics Platform teams. Integrate data from SAP S/4HANA, SAP ECC, SAP EWM, MES, o9, IMS, QMS/eQMS, food safety systems, audit tools, and IIoT platforms into reliable data products. Develop real-time dashboards, alerts, scorecards, and self-service reporting using Tableau, Power BI, Databricks SQL, and Python-based tools. Implement MLOps practices covering version control, CI/CD, model monitoring, drift detection, retraining, security, governance, and auditability. Measure and communicate business impact through quality, food safety, manufacturing, and supply chain KPIs. Requirements: The ideal candidate is an experienced data science professional with strong cloud engineering capabilities and a background in manufacturing, FMCG, quality analytics, or industrial AI. You should bring: 5–8+ years of experience in quality analytics, food safety analytics, manufacturing analytics, supply chain analytics, industrial AI, or data science within an operational or FMCG environment. Hands-on, production-level experience with Databricks and strong cloud expertise in Azure and/or AWS, particularly in data engineering, data science solution development, and solution architecture. Advanced Python skills, including pandas, NumPy, scikit-learn, time-series libraries, and TensorFlow or PyTorch. Strong SQL and PySpark skills, with experience building scalable data pipelines and governed data products in Databricks. Solid foundations in machine learning, statistics, time-series analysis, anomaly detection, classification, regression, forecasting, and model validation. Experience designing end-to-end cloud-based data science and engineering architectures, including ingestion, storage, processing, feature engineering, model training, deployment, monitoring, and governance. Experience developing dashboards and reporting solutions using Tableau, Power BI, Grafana, Databricks SQL, or Python-based tools. Familiarity with enterprise operational systems such as QMS/eQMS, IMS, MES, SAP ERP, CMMS, industrial historians, IIoT platforms, and audit management systems. Strong stakeholder management, communication, and data storytelling skills, with the ability to present technical insights to both plant-level teams and senior leadership. Knowledge of quality and food safety frameworks such as HACCP, GMP, Prerequisite Programmes (PRPs), hygienic zoning, allergen control, environmental monitoring, sanitation controls, and CAPA. Familiarity with internal Quality Policies is an advantage. Understanding of manufacturing and industrial frameworks, including Industry 4.0, IoT telemetry, Data Vault 2.0, and statistical process control. Experience with NLP techniques such as text classification, named entity recognition, topic modeling, embeddings, semantic search, and retrieval-augmented generation. Exposure to computer vision, image processing, object detection, segmentation, and defect classification in manufacturing or food production environments. Experience deploying Generative AI solutions using large language models, vector databases, prompt engineering, and responsible AI guardrails. Familiarity with optimization tools such as PuLP, Gurobi, CPLEX, and OR-Tools, or with simulation platforms. Knowledge of IL6S, Lean Six Sigma, SPC, CpK/PpK, measurement system analysis (MSA), 8D, 5-Why, FMEA, and structured root-cause analysis. Experience with MLOps tools and practices, including MLflow, model registries, data drift monitoring, feature stores, streaming analytics, and edge deployment. Familiarity with data governance platforms such as Collibra and Ataccama. Understanding of manufacturing and supply chain systems, including SAP S/4HANA, SAP ECC, SAP EWM, o9, MES, CMMS, APS, TMS, LIMS, QMS/eQMS, connected worker platforms, and industrial IoT systems. A Bachelor's or Master's degree in Data Science, Computer Science, Industrial Engineering, Chemical, Mechanical or Electrical Engineering, Food Science, Statistics, Operations Research, or a related discipline. A Master's degree or advanced qualification in Data Science, AI, Industrial Engineering, Quality Engineering, Food Safety, or Operations Research is preferred. Relevant certifications are advantageous, including Databricks Data Engineer, Machine Learning or Generative AI Engineer; Azure Data Scientist or Engineer Associate; AWS Machine Learning Specialty, Data Engineer Associate or Solutions Architect; Lean Six Sigma Green Belt or Black Belt; and HACCP, PCQI, FSSC 22000, ISO 22000, or BRCGS. Benefits: Full-time employment opportunity. Remote working option in India, with Mumbai also listed as a location. International relocation assistance available for candidates relocating to the country, in accordance with the applicable international transfer policy. Opportunity to work on advanced AI, machine learning, and industrial analytics projects with measurable business impact. Exposure to large-scale manufacturing, quality, food safety, and global supply chain operations. Collaboration with multidisciplinary teams across quality, engineering, manufacturing, research, and digital technology. Opportunity to contribute to production-deployed AI solutions and enterprise-wide digital transformation initiatives. Professional development through exposure to advanced cloud platforms, data science technologies, and industrial AI applications.