Senior Manager, Data Scientist – Supply Chain

Role Overview

• 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.
• Collaborate closely with Data Owners and supply chain subject matter experts to ensure that real-world constraints and business rules are accurately modeled.
• Develop Optimization & Simulation Algorithms: Design, build, and refine algorithms for supply chain planning optimization and scenario simulation.
• Build Predictive Machine Learning Models: Develop and deploy predictive models using supervised and unsupervised machine learning techniques to improve master data-driven decision making.
• 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.
• 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.
• End-to-End Model Deployment & Maintenance: Own the full lifecycle of data science solutions from development to deployment and ongoing maintenance. Monitor model and system performance, troubleshoot issues, and implement improvements or retraining as needed to maintain accuracy and efficiency.
• 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 to AI enablement in master data management.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics or a related field is required.
• An advanced degree (Master’s or Ph.D.) in a relevant discipline (e.g., OperationsResearch,Machine Learning) is highly preferred.
• Minimum of 5+ years of hands-on experience in data science, machine learning, or AI development, with a track record of deploying models or algorithms into production environments.
• Experience within supply chain, operations, or a similar domain is a plus, especially if it includes exposure to planning systems or master data management.
• Proficiency in programming and data analysis using languages and tools such as Python (including libraries like scikit-learn, pandas, TensorFlow/PyTorch, etc.) is required.
• Hands-on experience with agentic AI platforms, preferable Microsoft Azure AI Foundry and RPA tools to build automated workflows (UiPath, Power Automate).
• Demonstrated ability to develop machine learning models (regression, classification, clustering) and implement predictive analytics solutions.
• Experience with optimization techniques or tools (e.g., linear programming, constraint solving, Gurobi/CPLEX) for operations research problems is highly desired.
• Knowledge of NLP and generative AI technologies (working with large language models, Natural Language Understanding, etc.) is a strong plus.
• Comfortable working with big data technologies and cloud-based data platforms; able to query and manipulate data in SQL and utilize cloud services for model deployment.
• Exceptional analytical thinking and problem-solving skills, with the ability to tackle complex problems that may involve incomplete or imperfect data.
• Ability to manage autonomously multiple initiatives and deadlines concurrently.

Tech Stack

• Azure
• Cloud
• Pandas
• Python
• PyTorch
• RPA
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Health insurance
• 401(k) matching
• Flexible working hours
• Paid time off
• Remote work options