Career Area: Technology, Digital and Data

Job Summary

Provides technical leadership in applying Data Science, AI, and Machine Learning to transform large-scale data into actionable insights, intelligent automation, and business value across Packaging and related enterprise functions.

What You Will Do

- Lead the design, development, deployment, and optimization of AI/ML, Deep Learning, Computer Vision, and Generative AI solutions to address complex Packaging, Supply Chain, Logistics, and Engineering challenges.
- Direct large-scale data gathering, data mining, feature engineering, and data processing activities; create scalable data models and data pipelines.
- Explore, promote, and implement AI-driven capabilities using LLMs, Agentic AI Frameworks, NLP, semantic search, and advanced analytics techniques.
- Drive development and deployment of predictive, optimization, quality, sustainability, and automation solutions using machine learning and data science methodologies.
- Lead definition of business requirements, analytical scope, and solution architecture; translate business needs into scalable technical solutions.
- Collaborate with stakeholders, conduct workshops, and communicate actionable insights through dashboards, visualizations, and executive presentations.
- Establish MLOps, model governance, monitoring, retraining, and continuous improvement processes to ensure reliable production deployment of AI solutions.
- Conduct research and evaluation of emerging AI technologies, algorithms, and frameworks to improve solution effectiveness and business impact.

What You Have

Business Partnership Requirements Analysis

Knowledge of business analysis techniques and stakeholder engagement practices; ability to translate business needs into scalable data science and AI solutions.

Level: Extensive Experience

- Engages with business leaders, clients, and stakeholders to understand strategic priorities.
- Leads workshops, requirement-gathering sessions, and solution discovery activities.
- Defines analytical scope, success criteria, and technical requirements for AI initiatives.
- Translates complex business challenges into data science, machine learning, and automation solutions.
- Effectively communicates technical concepts to executive, technical, and business audiences.
- Partners with cross-functional teams to drive adoption and business value realization.

Query Database Access Tools





Knowledge of data management systems and data access technologies; ability to retrieve, transform, and optimize enterprise data for analytics and AI applications.

Level: Extensive Experience

- Writes, optimizes, and supports complex SQL queries across multiple databases and data sources.
- Works extensively with structured and unstructured data environments.
- Designs data retrieval and transformation strategies supporting AI and analytics workloads.
- Consults on query optimization, performance tuning, and database best practices.
- Utilizes big data technologies and distributed data processing frameworks.
- Evaluates database technologies and architectures supporting AI initiatives.

Data Analysis Statistical Modeling

Knowledge of statistical methods, predictive analytics, and data-driven decision-making; ability to transform data into meaningful business insights.

Level: Working Experience

- Performs advanced statistical analysis, predictive modeling, and machine learning experimentation.
- Uses statistical techniques to identify patterns, trends, anomalies, and business opportunities.
- Translates complex analytical findings into actionable business recommendations.
- Develops metrics, KPIs, and analytical frameworks to support strategic decisions.
- Evaluates model accuracy, effectiveness, and business impact using statistical methodologies.
- Communicates analytical insights to both technical and non-technical stakeholders.

Artificial Intelligence Machine Learning

Knowledge of machine learning, deep learning, generative AI, computer vision, and agentic frameworks; ability to develop, deploy, and manage AI-based solutions that drive business outcomes.

Level: Working knowledge

- Leads the design and deployment of Machine Learning, Deep Learning, Computer Vision, and Generative AI solutions.
- Develops and implements LLM-based applications utilizing Agentic AI, NLP, embeddings, summarization, and semantic search technologies.
- Selects, trains, evaluates, and optimizes models using TensorFlow, PyTorch, Scikit-Learn, PySpark MLlib, and related frameworks.
- Monitors model performance and implements retraining,



scalability, and error-handling strategies.
- Coaches and mentors teams on AI technologies, methodologies, and best practices.
- Applies AI solutions to solve complex packaging, logistics, engineering, and supply chain business challenges.

Programming Languages Software Development

Knowledge of programming concepts, software development practices, and application development frameworks; ability to build scalable AI-enabled applications and enterprise solutions.

Level: Working Experience

- Demonstrates expertise in Python, SQL, PySpark, Apache Spark, APIs, and distributed computing technologies.
- Develops scalable AI applications using Streamlit, Gradio, and cloud-native architectures.
- Integrates AI services with enterprise business systems and backend platforms.
- Implements software engineering best practices, code reviews, testing frameworks, and CI/CD processes.
- Guides teams in selecting development tools, frameworks, and coding standards.
- Oversees development activities ensuring quality, maintainability, and performance.

Cloud Data Engineering

Knowledge of cloud platforms, data engineering practices, and enterprise-scale distributed systems; ability to design and implement scalable AI and analytics solutions.

Level: Working Knowledge

Designs and deploys end-to-end distributed solutions on Azure, AWS, Databricks, and cloud-native environments.

- Works with relational and non-relational databases, data warehouses, big data platforms, and caching technologies.
- Develops and optimizes large-scale data pipelines and feature engineering workflows.
- Utilizes cloud-native architectures to support AI, analytics, and automation solutions.
- Implements scalable data processing and storage solutions supporting enterprise AI systems.
- Evaluates emerging cloud technologies and recommends improvements.

MLOps Production Deployment

Knowledge of model lifecycle management, MLOps frameworks, and deployment architectures; ability to operationalize AI solutions at enterprise scale.

Level: Working Knowledge

- Implements MLOps frameworks using MLflow, Docker, Kubernetes, Git, Azure DevOps, and CI/CD pipelines.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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