Global Supply Chain Data Scientist

Job Description

As the Global Supply Chain Data Scientist, you will develop and deploy analytics, automation, and predictive models that improve logistics cost, service, and operational scalability across the global network. You will lead cross‑functional digital initiatives from concept to rollout - translating logistics problems into data products (dashboards, models, alerts) and ensuring solutions are implemented, adopted, and sustained through strong project governance and change management.

Principle Duties/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

Qualifications

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).

Preferred Qualifications:

Experience in supply planning and forecasting within a commercial setting.

Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.

Knowledge of advanced machine learning techniques and frameworks.

Additional Details

This job has a full time weekly schedule.

Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/locations

Travel Required:

25% of the Time

Shift:

Day

Duration:

No End Date

Job Function:

Administration