Key Responsibilities

Supply Chain Data Analysis and Interpretation
Analyze large and complex datasets from ERP, MES, and LIMS systems to identify trends, patterns, and actionable insights for improving demand forecasting

Model Development for Supply Chain Optimization
Develop and validate predictive models and algorithms for demand sensing, capacity planning, lot traceability, and risk mitigation across global pharmaceutical

Cross-Functional Collaboration
Work closely with manufacturing, quality, clinical supply, and distribution teams to understand operational challenges and deliver tailored analytics solutions tha standards.

Data Management & Compliance
Implement robust data governance practices to maintain data integrity and quality, ensuring adherence to FDA, EMA, and other regulatory requirements for life science

Reporting and Visualization
Create intuitive dashboards and reports for real-time supply chain monitoring, leveraging tools like Power BI, Tableau, or Spotfire to communicate insights to stakeholders

Innovation in Supply Chain Analytics
Stay updated on the latest developments in AI/ML for supply chain, including digital twin modeling, predictive maintenance, and blockchain for serialization, appl complex challenges.

Preferred Qualifications

14+ years of experience in Data & Analytics, with a strong focus on Life Sciences Supply Chain.

Hands-on experience with big data technologies (Hadoop, PySpark) and cloud-based analytics platforms (AWS, Azure).

Proficiency in Databricks, Python, R, and AWS/Azure services such as SageMaker, Glue, Lambda, Step Functions, and EMR.

Deep understanding of pharmaceutical supply chain processes, including cold chain logistics, serialization, and clinical trial supply.

Familiarity with data visualization tools (Tableau, Power BI) and supply chain planning systems (SAP IBP, Kinaxis).

Knowledge of regulatory compliance (GxP, FDA, EMA) and experience working with structured and unstructured data from ERP and manufacturing systems.