Sr Manager Data Scientist Supply Chain

ROLE SUMMARY The Data Scientist role will be part of the Supply Planning Data Intelligence organization and will be responsible for building, training, deploying, and maintaining AI/ML models and automation tools for supply chain master data. Acting as part of the "tech engine" of the Intelligent Data Factory initiative, this role develops advanced analytics solutions—such as optimization algorithms, predictive models, generative AI tools, and intelligent data agents—to drive autonomous and self-healing data pipelines. The Data Scientist works globally across multiple functions to enable a data-driven and automated supply chain, progressing through a phased delivery model that starts with foundational quick wins and evolves into a mature ecosystem of self-learning, autonomous data agents. ROLE RESPONSIBILITIES • Develop Optimization & Simulation Algorithms: Design, build, and refine algorithms for supply chain planning optimization and scenario simulation. This includes creating AI models that optimize planning parameters (e.g., safety stock levels, lot sizes) and simulating planning decisions under various scenarios. Collaborate closely with Data Owners and supply chain subject matter experts to ensure that real-world constraints and business rules are accurately modeled. • Build Predictive Machine Learning Models: Develop and deploy predictive models using supervised and unsupervised machine learning techniques to improve master data-driven decision making. Examples include predicting key master data inputs (such as transit lead times or shelf-life constraints), implementing anomaly detection systems for data quality, and clustering techniques for pattern recognition. Maintain end-to-end data pipelines, ensuring models are regularly retrained and validated with clean, well-labeled data. • Implement Generative AI & NLP Solutions: Create and integrate generative AI and natural language processing solutions to automate and enhance data management processes. Use large language models and related frameworks to auto-generate documentation or code (for data pipelines), suggest or populate master data values, and enable natural language interfaces (chatbot or Q&A tools) that allow users to query and interact with data assets more intuitively. • Develop Intelligent Data Agents & Automation Bots: Design and deploy AI agents and robotic process automation (RPA) bots to handle repetitive master data tasks and proactively resolve data issues. Build bots that can automatically create, validate, or cleanse master data records. Engineer autonomous AI agents that monitor real-time data signals and trigger actions or alerts (for example, identifying inconsistencies and initiating corrections) to keep master data accurate and up-to-date. Integrate these agents with supply chain planning systems (e.g., Kinaxis) to close the feedback loop and ensure that planning adjustments are executed based on the latest data insights. • End-to-End Model Deployment & Maintenance: Own the full lifecycle of data science solutions from development to deployment and ongoing maintenance. Ensure that all AI/ML solutions are deployed in the appropriate production environment and operate with high reliability and performance. Monitor model and system performance, troubleshoot issues, and implement improvements or retraining as needed to maintain accuracy and efficiency. Establish self-healing mechanisms in data pipelines, so that the system can automatically address or alert on anomalies without manual intervention. • Drive Innovation & Phased AI Enablement: Contribute to the continuous innovation of the Intelligent Data Factory by staying abreast of cutting-edge AI techniques and identifying opportunities to enhance automation. Support a phased delivery approach