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Agilent Technologies

Provides eProcurement automation and hosted catalogs

Decision Support Data Scientist - Global Manufacturing Analytics

Data ScienceConfirmed live in the last 24 hours
Full-timeYishun, SingaporeNo salary listed

About the Job

Requirements

  • A Bachelor's or Master's degree in Data Science, Statistics, Operations Research, Computer Science, Engineering, Business Analytics, or a related field is required.
  • At least 8 years of experience in data science, decision science, manufacturing analytics, business analytics, or equivalent work experience is required.
  • Strong analytical and problem-solving skills, including the ability to frame complex decisions, test hypotheses, challenge assumptions, and translate data into actionable recommendations.
  • Experience producing executive-level performance insights, decision-support analyses, forecasts, scenarios, and management narratives for senior stakeholders.
  • Hands-on experience in statistics, forecasting, scenario modeling, diagnostic and predictive analytics, machine learning, and artificial intelligence, with practical application in manufacturing or operations.
  • Experience defining and validating key performance indicators, baselines, targets, thresholds, and leading indicators, and assessing uncertainty, accuracy, bias, stability, explainability, and business relevance.
  • Ability to explain analytical methods, limitations, implications, and recommendations to non-technical stakeholders and senior leaders.
  • Proficiency in programming and analytical languages such as Python, SQL, or R.
  • Experience using enterprise data-visualization tools such as Tableau, Power BI, Qlik, Spotfire, or similar to communicate insights and support decision-making.
  • Ability to assess and work with complex enterprise data, including data quality, semantic consistency, lineage, and limitations affecting analytical conclusions.
  • Ability to lead cross-functional analytical workstreams with disciplined planning, stakeholder alignment, peer review, and value assessment.
  • Ability to operate under pressure and deliver rapid analytical prototypes or minimum viable products to validate concepts, de-risk assumptions, and confirm direction.

Responsibilities

  • Own the analytical quality and integrity of Control Tower insights, ensuring conclusions, forecasts, scenarios, and recommendations are accurate, explainable, decision-relevant, and supported by appropriate evidence.
  • Structure ambiguous business questions into clear decision problems by defining the decisions, options, assumptions, hypotheses, evidence, and success criteria required for analysis.
  • Develop Control Tower decision-intelligence and performance insights across productivity, throughput and capacity; quality, yield and operational risk; delivery, shipment and backlog performance; cost, margin and resource drivers; and manufacturing network and site performance.
  • Define and validate key performance indicator logic, baselines, targets, thresholds, and leading indicators, ensuring consistent calculation, appropriate interpretation, and relevance to management decisions.
  • Select and apply statistics, forecasting, diagnostic analytics, machine learning, and artificial intelligence based on the business question; quantify uncertainty, confidence, bias, stability, sensitivity, and decision trade-offs.
  • Produce executive performance narratives, driver analyses, early-warning signals, scenario implications, and evidence-based recommendations, distinguishing meaningful signals from noise and clarifying where management attention is required.
  • Build and validate analytical prototypes and models using Python, SQL, R, business intelligence tools, and cloud technologies; document methods, assumptions, limitations, and validation results to support reproducibility and responsible use.
  • Monitor analytical performance after deployment, including forecast accuracy, bias, stability, drift, data quality, and continued business relevance; recommend recalibration, enhancement, or replacement when required.
  • Partner with Analytics, the Project Lead, Engineering, and Operations and Management teams to align analytics with operational priorities, global standards, and enterprise decision processes.
  • Lead the analytical workstream from problem framing through validation and value assessment, supporting cross-functional alignment and adoption while the Analytics Lead retains accountability for end-to-end Control Tower delivery and user experience.

Desired Qualifications

  • Experience in manufacturing operations, industrial engineering, quality management, supply chain, finance, or enterprise performance management.
  • Experience supporting Control Tower, decision-intelligence, performance-management, integrated business planning/sales and operations planning, enterprise planning, or operational-intelligence initiatives.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud.
  • Experience integrating and interpreting data from enterprise resource planning, manufacturing execution systems, shopfloor systems, enterprise data platforms, or operational workflows.
  • Familiarity with Industry 4.0 technologies, including industrial internet of things, digital twins, and automation.
  • PMP, Scrum Master, or relevant analytics certification, with experience supporting large-scale, enterprise-level initiatives.

About the Company

Agilent Technologies

Provides eProcurement automation and hosted catalogs

Agilent Technologies provides eProcurement services that help businesses buy supplies more efficiently. It offers a suite of electronic procurement tools that let clients load their pricing into existing purchasing platforms, access a hosted catalog, and complete transactions digitally without paper. The service connects pricing data to clients’ procurement systems, delivers an online catalog, and supports automated, paperless purchasing workflows. This makes purchasing faster and reduces manual steps. Compared with competitors, Agilent emphasizes automation, quick connectivity between pricing, catalogs, and purchasing platforms, and a reliable, scalable experience backed by its broad product range. The company aims to improve client productivity and lower procurement costs, helping customers buy what they need more easily while keeping prices clear and consistent.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1999

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