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
- Secure, 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr Supply Chain Data Scientist (Bengaluru)
🏢 MKS Instruments
📍 Bengaluru