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Agilent Technologies

Global Supply Chain Data Scientist

Agilent Technologies
Life SciencesSingapore-YishunOnsitePosted 4 weeks ago
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About the role

AI summarised

Develop and deploy analytics, automation, and predictive models to enhance logistics cost, service levels, and operational scalability across a global network. This role involves leading cross-functional digital initiatives from concept to rollout, translating complex logistics challenges into actionable data products.

Life SciencesOnsite

Key Responsibilities

  • Provide data-driven insights, dashboards, and scenario modeling to support logistics leaders across warehousing, freight, distribution, and service parts.
  • Build and maintain logistics KPI views tracking warehouse performance, freight analytics, productivity, and operational health with root-cause analysis.
  • Develop 'control tower'-style analytics to provide early warning indicators regarding delays, backlog risk, capacity constraints, and throughput issues.
  • Drive automation and advanced analytics using Python, SQL, and BI tools to scale insight generation and minimize manual reporting.
  • Create and maintain predictive models for logistics use cases, including throughput forecasting, transportation analytics, and operational scenario planning.
  • Drive continuous improvement in data quality, metric reliability, and supply/inventory analytics across global regions.
  • Partner with Supply Chain & Logistics SMEs and digital stakeholders to ensure analytics align with organizational standards, governance, and process maturity.

Requirements

  • Bachelor's or Master's Degree in Data Science, Statistics, Computer Science, or a related field.
  • At least 8 years of experience in data science or equivalent professional work experience.
  • Proficiency in programming languages including Python, R, or SQL.
  • Experience with data visualization tools such as Tableau, Power BI, Qlik, or Spotfire.
  • Extensive knowledge in predictive modeling, machine learning, and statistical analysis.
  • Strong analytical and problem-solving abilities to interpret complex datasets.
  • Excellent verbal and written communication skills, capable of presenting technical information to non-technical stakeholders.
  • Demonstrated ability to lead cross-functional work with strong project management discipline.