Senior Supply Chain Data Scientist

Senior Data Scientist – Digital Supply Chain

Job Summary

• Enable Global Supply Chain decision‑making by delivering business intelligence, data engineering, and applied analytics solutions.
• Focus on Power BI reporting, data modeling, ETL pipelines, and Python-based analytics, with selective predictive analytics where it provides tangible business value.
• Partner closely with Business Process (BPM), Master Data Management (MDM), Supply Chain Operations, and IT to ensure analytics are trusted, scalable, and operationally embedded across SAP and Oracle environments.

A Day in the Life

• Start the day reviewing Power BI dashboard refreshes, checking data quality, and addressing any overnight data or pipeline issues.
• Meet with supply chain stakeholders (planning, procurement, operations) to clarify reporting needs, KPI definitions, or new analytics requests.
• Build or enhance Power BI datasets and semantic models, optimizing DAX and data structures for performance and usability.
• Develop or maintain Python- and SQL-based ETL pipelines, integrating data from SAP, Oracle, and other enterprise sources into analytics-ready models.
• Collaborate with MDM and BPM teams to validate master data, align definitions, and ensure process-consistent reporting.
• Support AI or advanced analytics initiatives by preparing datasets, validating outputs, and operationalizing insights into dashboards or workflows.
• Document data logic, models, and metrics, and occasionally train or support business users to improve analytics adoption.
• End the day prioritizing upcoming analytics work and coordinating with IT or digital teams on dependencies and delivery timelines.

Key Responsibilities

• Business Intelligence & Reporting
• Design, develop, and maintain Power BI dashboards, reports, and semantic models for supply chain KPIs (OTD, inventory, shortages, supplier performance, planning metrics).
• Ensure reporting is accurate, governed, and aligned with enterprise analytics standards.
• Translate business questions into clear, actionable visual insights.
• Data Engineering & Modeling
• Build and maintain data models and ETL pipelines using Python, SQL, and enterprise data platforms.
• Integrate and harmonize data from ERP systems (SAP, Oracle), supply chain data marts, and analytics platforms.
• Ensure data quality, lineage, and documentation in partnership with MDM and IT teams.
• Applied Analytics
• Deliver descriptive and diagnostic analytics, with selective predictive use cases (e.g., trend analysis, lead-time insights, planning support).
• Support AI/ML initiatives by enabling clean data, validating results, and embedding insights into operational reporting.
• Cross‑Functional Collaboration
• Act as a bridge between business users and IT, ensuring analytics solutions are both technically sound and business-relevant.
• Partner with supply chain teams to prioritize analytics demand and deliver incremental, high-impact solutions.
• Operational Excellence
• Monitor dashboard reliability, data refresh cycles, and pipeline performance.
• Contribute to reusable datasets, analytics standards, and continuous improvement of DSC analytics capabilities.

Required Qualifications

• Education & Experience
• Bachelor’s degree in Data Analytics, Computer Science, Engineering, Supply Chain, or related field.
• 8+ years of experience in business intelligence, data analytics, or data engineering roles.
• Technical Skills
• Strong proficiency in Power BI (data modeling, DAX, performance optimization).
• Hands-on experience with data modeling, ETL processes, SQL, and Python.
• Experience working with ERP data (SAP, Oracle) and enterprise data platforms.
• Business & Analytical Skills
• Solid understanding of supply chain processes and metrics.
• Ability to translate business needs into scalable analytics solutions.
• Strong attention to data accuracy, consistency, and usability.
• Collaboration & Communication
• Ability to work effectively with global, cross-functional teams.
• Clear communication skills for explaining insights to non-technical audiences.

Preferred Qualifications

• Experience in global manufacturing or supply chain environments.
• Familiarity with cloud data platforms (Azure, AWS, Fabric, Databricks).
• Exposure to AI/ML concepts and analytics enablement for AI-driven initiatives.
• Experience with data governance, master data, or process analytics.
• Agile or project-based delivery experience.

Success Measures

• Analytics Reliability & Adoption
• Stable, trusted Power BI dashboards with strong business adoption.
• Reduced data issues and improved confidence in supply chain reporting.
• Business Impact
• Analytics solutions that materially support better planning, procurement, inventory, and operational decisions.
• Positive stakeholder feedback on clarity, usefulness, and responsiveness.
• Scalability & Enablement
• Reusable data models and pipelines that reduce manual effort and improve delivery speed.
• Effective collaboration with BPM, MDM, AI, and IT teams to enable end-to-end digital supply chain analytics.