About the role

AI summarised

The Global Supply Chain Data Scientist develops and deploys analytics, automation, and predictive models to improve logistics cost, service, and operational scalability across a global network. This role leads cross-functional digital initiatives, translating logistics problems into data products such as dashboards and models, and ensures implementation through strong project governance and change management.

Life SciencesFull-timeGeneral

Key Responsibilities

Provide data-driven insights, dashboards, and scenario modeling to support logistics leaders and operations teams (warehouses, freight, distribution, service parts) in day-to-day and enterprise decision-making
Build and maintain logistics KPI views covering warehouse performance, freight analytics, productivity, and operational health, with clear exception signals and root-cause paths
Develop 'control tower'-style analytics that enable early warning indicators (delays, backlog risk, capacity risk, throughput constraints)
Drive automation and advanced analytics using Python, SQL, and BI tools to scale insight generation and reduce manual reporting effort
Create and maintain models for logistics use cases such as: Throughput and productivity forecasting, Transportation analytics, Operational scenario planning / simulations aligned with the digital roadmap direction (e.g., scenario planning, 'digital twins')
Drive continuous improvement in data quality, definitions, and metric reliability to ensure consistent decision making across regions/sites
Partner with supply chain & logistics SMEs and digital stakeholders to align analytics with standards, governance, and process maturity efforts
Drive continuous improvement in supply and inventory analytics, data quality, and model reliability
Drive automation and advanced analytics, leveraging Python, SQL, and BI tools to scale insights
Partner with Supply Chain & Logistics to align analytics with standards, governance, and process maturity efforts

Requirements

Bachelor's or Master's Degree in Data Science, Statistics, Computer Science, or a related field
Typically, at least 8 years of experience in data science or equivalent work experience
PMP certification preferred, with proven experience managing large-scale, enterprise-level projects
Extensive knowledge in predictive modeling, machine learning, and statistical analysis
Proficiency in programming languages such as Python, R, or SQL
Experience with data visualization tools like Tableau, Power BI, Qlik, Spotfire or similar
Strong Analytical and problem-solving skills with the ability to interpret complex data
Excellent communication skills, both written and verbal, with the ability to present technical information to non-technical stakeholders
Ability to work independently and as part of a team in a fast-paced environment
Strong executive presence and leadership skills
Excellent communication skills, with the ability to effectively engage with different levels of management
Demonstrated ability to lead cross-functional work with strong program/project management discipline (planning, execution, stakeholder management)
Experience in supply planning and forecasting within a commercial setting (preferred)
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud (preferred)
Knowledge of advanced machine learning techniques and frameworks (preferred)