Sr Manager Data Scientist Supply Chain

996 Pfizer SLU (Spain); Madrid; On-site; EUR 90,000 - 120,000; Full time

Job summary

Pfizer Inc. seeks a Data Scientist to join the Supply Planning Data Intelligence team. You will build, train, deploy, and maintain AI/ML models and automation tools to optimize master data in a multinational supply chain.

This role acts as the tech engine for the Intelligent Data Factory initiative, delivering progressively autonomous data pipelines and self-learning systems. Responsibilities include designing optimization algorithms, predictive modeling, and integrating RPA bots and

Qualifications

• Bachelor’s degree required in a quantitative field; advanced degree preferred.
• Minimum of 5+ years hands-on experience in data science applied to supply chain or manufacturing domains.

Responsibilities

• Design, build, and refine optimization and simulation algorithms for supply chain planning.
• Develop and deploy predictive models to improve master data-driven decision making.
• Implement generative AI and NLP solutions to automate data management and create intuitive interfaces.
• Create intelligent data agents and RPA bots to automate repetitive master data tasks.
• Oversee end-to-end model deployment and maintainance with self-healing data pipelines.
• Contribute to phased AI enablement across multiple maturity stages with measurable quick wins.

Education

Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics or a related field

Master’s or Ph.D. in Operations Research / Machine Learning or related field preferred

Tools

Kinaxis

Job description

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 to AI enablement in master data management: in Phase1, focus on delivering foundational machine learning models and basic RPA bots that yield quick wins; in Phase2, help implement more advanced optimization algorithms and generative AI "copilots" to significantly increase process autonomy; in Phase3, assist in cultivating a fully mature ecosystem of self‑learning, autonomous data agents that require minimal human intervention. Through each phase, ensure learnings are captured and fed back into the development cycle to drive continuous improvement. This role is the main lead to hand over validated and tested solutions into production with the Digital/IT team, becoming the business owner of the deployed solutions.

QUALIFICATIONS

• Education & Experience: 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