Role Summary:
As the Data Lead, you will define the organization's data strategy, integrating data from a variety of industrial systems, including ERP, MES, PLM, SAP, and IoT, to support end-to-end visibility and actionable insights. You will lead data engineering, data science, and machine learning teams to create a scalable, secure, and high-performance data infrastructure.

Key Responsibilities:
1. Develop and Execute Enterprise Data Strategy
•    Data Strategy Formulation: Create a comprehensive data strategy aligned with business objectives, focusing on leveraging data from ERP, MES, PLM, and IoT systems for operational excellence and innovation.
•    Data Architecture Design: Design a scalable, flexible data architecture that accommodates diverse data sources, supports real-time analytics, and enables AI/ML applications in manufacturing and logistics.
•    Technology Stack Selection: Evaluate and select appropriate technologies for data storage, processing, and analytics, considering both on-premise and cloud solutions to support the enterprise data ecosystem.

2. Implement Data Integration and Management Solutions
•    System Integration and Data Unification: Lead the integration of data from ERP (e.g., SAP), MES, PLM, and IoT systems, enabling a unified view of operations. Ensure data flows smoothly across systems to provide complete, timely, and accurate information.
•    Data Transformation and ETL Processes: Guide the development of ETL pipelines to extract, transform, and load data from ERP, MES, and PLM systems into centralized storage, ready for analytics and machine learning applications.
•    Real-Time Data Processing: Design real-time data processing capabilities to support applications such as predictive maintenance, production monitoring, and anomaly detection. Utilize tools like Apache Kafka, Spark Streaming, and Flink for low-latency processing and timely insights.

3. Drive AI and Machine Learning for Operational Insights
•    Predictive Maintenance and Operational Analytics: Develop AI models to enable predictive and prescriptive analytics, leveraging data from IoT, MES, and ERP systems to detect early signs of equipment wear, anticipate maintenance needs, and optimize production schedules.
•    Process Optimization and Automation: Lead data science initiatives to support process optimization, waste reduction, and throughput improvement. Build prescriptive analytics models that generate actionable recommendations for efficiency gains in manufacturing and logistics.
•    AI for Quality Control and Demand Forecasting: Implement machine learning models for quality control and demand forecasting, optimizing inventory management, and aligning production output with market demand. Use AI to identify patterns in historical data for predictive insights.

4. Manage Data Governance, Compliance, and Security
•    Data Governance Across Enterprise Systems: Define and enforce data governance policies, including role-based access controls, data lineage, and audit trails across ERP, MES, SAP, and PLM platforms. Ensure data usage aligns with enterprise regulations and data governance frameworks.
•    Regulatory Compliance and Security: Ensure compliance with regulatory standards such as GDPR, CCPA, and industry-specific data requirements. Collaborate with IT and cybersecurity teams to secure data across platforms, protecting sensitive operational and customer data.
•    Data Quality Assurance: Establish data quality protocols and automated validation processes to ensure the accuracy and reliability of data, especially as it flows from industrial systems like MES and ERP. Use automated checks to identify and resolve data quality issues early in the pipeline.

5. Lead Data Engineering and Infrastructure Development
•    Data Pipeline and Workflow Management: Oversee the Data Engineering team in building ETL pipelines, ensuring efficient data flows across ERP, MES, SAP, PLM, and IoT systems. Focus on data integration, integrity, and latency reduction to optimize data availability.
•    Data Warehousing and Storage Solutions: Develop robust data warehousing solutions to support both structured and unstructured data from industrial systems, ensuring scalability and performance. Implement a hybrid cloud strategy to accommodate both on-premise and cloud-based data storage needs.
•    Cloud and Edge Computing Solutions: Leverage cloud and edge computing to build flexible data storage and processing systems that support enterprise data from ERP, MES, and IoT sources. Ensure data infrastructure is optimized for both centralized analytics and distributed processing.

6. MLOps and Model Deployment for Edge and Cloud
•    MLOps for Continuous Improvement: Implement MLOps practices to automate model deployment, monitoring, and retraining for machine learning models supporting Industry 4.0. Set up CI/CD pipelines for efficient model management and iteration.
•    Edge AI Model Deployment: Deploy AI models on edge devices for real-time inference in use cases like quality control, predictive maintenance, and anomaly detection. Collaborate with IoT and Edge AI teams to optimize models for low-latency applications.
•    Model Monitoring and Feedback Loops: Establish continuous monitoring and feedback loops to track model performance in production. Develop processes for regular model updates to address data drift and changing operational conditions.

7. Data Analytics and Insights Generation for Industry 4.0
•    Advanced Operational Analytics: Lead the development of analytics capabilities that track and analyze KPIs across ERP, MES, PLM, and IoT systems. Use data to inform insights into productivity, inventory, and manufacturing efficiency.
•    Visualization and Reporting: Oversee the creation of dashboards and reports that present actionable insights to stakeholders. Use tools like Power BI, Tableau, and custom analytics platforms to visualize complex data sets in a comprehensible manner.
•    Collaboration with Cross-Functional Teams: Work closely with Operations, Manufacturing, and IT teams to embed data insights into decision-making processes. Support the adoption of data-driven practices across departments for enhanced efficiency and strategic alignment.

8. Team Leadership and Development
•    Lead and Mentor Data Teams: Manage a team of data engineers, data scientists, and ML engineers, promoting a collaborative and innovation-driven culture. Set clear performance objectives, provide regular feedback, and foster continuous learning.
•    Cross-Functional Collaboration: Encourage collaboration between data teams and other departments, including IT, Product, and Manufacturing, to ensure alignment on data needs and project goals. Promote a data-first culture across the organization.
•    Recruitment and Retention: Work with HR to recruit top data talent with expertise in ERP, MES, and IoT systems. Develop a retention strategy to maintain a high-performing, motivated team.

9. Innovation and Research in Data Science and AI for Industrial Automation
•    Stay Current with Emerging Technologies: Keep the organization at the forefront of data science, machine learning, and IoT innovations. Drive experimentation with new technologies that support Industry 4.0 applications in manufacturing and automation.
•    Initiate R&D Projects for Innovation: Lead R&D initiatives focused on advanced analytics and AI, such as reinforcement learning, unsupervised learning, and generative models. Evaluate proof-of-concept projects for application in industrial automation.
•    Community and Open-Source Engagement: Encourage team participation in open-source projects and data science forums. Promote knowledge-sharing across the data community to support the latest advancements and best practices.

10. Performance Optimization, Security, and Compliance
•    Optimize Data Infrastructure for Industrial Use: Ensure that data infrastructure is optimized for high-performance, real-time analytics in industrial environments. Focus on system reliability and low latency for IoT and ERP data handling.
•    Enterprise Data Security: Oversee the implementation of advanced security protocols to protect sensitive operational and business data across ERP, MES, and IoT platforms. Ensure compliance with industry regulations for data security and privacy.
•    Document Compliance and Audit Practices: Maintain thorough documentation of data practices and processes. Conduct regular audits to ensure compliance with industry regulations and internal data policies.

Required Qualifications:
•    Education: Bachelor's or Master's degree in Data Science, Computer Science, Engineering, or a related field. Advanced degrees or certifications in data engineering, AI, or Industry 4.0 are preferred.
•    Experience: • 10+ years of experience in data science, data engineering, or enterprise data management, with at least 5 years in a leadership role. • Demonstrated expertise in managing data from ERP (e.g., SAP), MES, PLM, and IoT systems, with a deep understanding of Industry 4.0 applications. • Strong background in designing data architectures and managing large-scale data systems for predictive analytics, process optimization, and operational intelligence.
Technical Skills:
•    Enterprise Data Management: Proficiency with ERP systems like SAP, MES, PLM integration, and data engineering tools (e.g., Apache Kafka, Spark, Hadoop). Strong experience in data warehousing solutions like Snowflake, Redshift, or similar.
•    IoT and Edge Processing: Familiarity with IoT protocols, SCADA systems, and edge computing for real-time data collection and processing. Experience deploying AI models on edge devices to support real-time inference in manufacturing environments.
•    Machine Learning and AI: Deep understanding of ML/DL frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn) and MLOps practices for seamless deployment. Expertise in developing and operationalizing predictive and prescriptive models for industrial applications.
•    Programming Skills: Proficiency in Python and SQL, with additional knowledge of Scala, Java, or R as a plus. Experience with data pipeline scripting and API integration for seamless data flow between systems.
•    Cloud Computing: Extensive experience with cloud platforms (e.g., AWS, Azure, GCP) for big data processing, storage, and analytics. Familiarity with cloud-native services for data lakes, data warehousing, and serverless computing.
•    Data Visualization: Proficiency in data visualization tools such as Tableau, Power BI, or D3.js for creating interactive dashboards and reports to communicate insights effectively.
•    Big Data Technologies: Hands-on experience with big data technologies like Hadoop, Hive, and NoSQL databases (e.g., MongoDB, Cassandra) for handling large-scale industrial data sets.
•    Data Governance and Security: Strong knowledge of data governance frameworks, data quality management, and security protocols for protecting sensitive industrial and operational data.
•    API Development and Integration: Experience in designing and implementing RESTful APIs for data exchange between different systems and platforms in an industrial setting.
•    Containerization and Orchestration: Familiarity with Docker and Kubernetes for containerizing and orchestrating data applications and services across cloud and on-premise environments.
•    Time Series Analysis: Expertise in analyzing time-series data from industrial sensors and IoT devices. Proficiency in using specialized libraries and techniques for forecasting, anomaly detection, and trend analysis in manufacturing processes.
•    Natural Language Processing (NLP): Experience applying NLP techniques to extract insights from unstructured data sources like maintenance logs, customer feedback, and operational reports to enhance decision-making in industrial settings.
•    Distributed Computing: Knowledge of distributed computing frameworks like Apache Spark and Dask for processing large-scale industrial datasets efficiently across clusters.
•    Digital Twin Technology: Understanding of digital twin concepts and experience in implementing data-driven virtual models of physical assets or processes for simulation, optimization, and predictive maintenance in manufacturing environments.
Preferred Qualifications:
•    MLOps and CI/CD Implementation: Experience with CI/CD pipelines and MLOps practices for automating model development and deployment. Proficiency in using tools like MLflow, Kubeflow, and other MLOps platforms for large-scale data operations.
•    Industry 4.0 and Compliance Knowledge: Strong understanding of Industry 4.0 concepts, standards, and data regulations, including GDPR and CCPA, as well as industry-specific compliance requirements.
•    Project Management and Leadership: Proven experience managing large-scale data projects, with expertise in Agile methodologies and project management tools to oversee cross-functional collaborations.
•    Open-Source and Community Engagement: A track record of contributing to open-source projects and participating in industry forums. Familiarity with the latest trends and advancements in data science, IoT, and industrial automation.