Decision Support Data Scientist Global Manufacturing Analytics - agilent

OpenTalent; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

OpenTalent seeks an early-career Decision Support Data Scientist to support the Manufacturing Analytics and Strategy Execution team. You will leverage decision science, statistics, forecasting, ML, and AI to generate insights that inform management decisions and performance management.

You will translate operational questions into analytical tasks, develop KPI logic, and present clear visualisations and scenarios.

Qualifications

• Experience applying decision science and statistics to manufacturing analytics.
• Ability to translate operational questions into analytical tasks and success criteria.
• Proficiency with Python/SQL/R and data visualization tools.

Responsibilities

• Support development and validation of Control Tower insights with evidence-backed analyses.
• Translate business questions into analytical tasks, hypotheses, and data needs.
• Develop decision-support analyses across productivity, throughput, cost, and network performance.
• Define KPI logic, baselines, targets, and leading indicators with clear interpretation.
• Prepare visualisations and narratives highlighting meaningful signals and actions.
• Build and test analytical prototypes using Python/SQL/R and cloud tech; document methods.

Skills

Decision science

Statistics

Forecasting

Machine learning

Data exploration

Python

SQL

R

BI tools

Cloud technologies

Tools

Python

SQL

R

BI tools

Cloud technologies

Job description

Job Description

As a Decision Support Data Scientist within the Manufacturing Analytics and Strategy Execution (MASE) team, you will contribute to the development and validation of high-quality insights that support management decision-making and performance management. You will apply decision science, statistics, forecasting, advanced analytics, machine learning, AI, and business understanding to help strengthen manufacturing performance and enable faster, more consistent decisions.

Working with experienced team members as part of the Global Manufacturing Control Tower, you will translate operational data into clear performance insights, driver analyses, forecasts, scenarios, early-warning signals, and recommendations. You will help ensure that analytical outputs are reliable, explainable, and relevant to the decisions leaders need to make.

This role is designed for an early-career professional who is curious, analytical, and eager to learn. You will build practical experience in manufacturing analytics while gradually taking ownership of defined decision-support use cases and contributing to the question: what is happening, why is it happening, what may happen next, and what actions should be considered?

Principal Duties / Responsibilities

Support the development and validation of Control Tower insights, helping ensure that analyses, forecasts, scenarios, and recommendations are accurate, explainable, and supported by appropriate evidence.

Work with senior Data Scientists and business stakeholders to translate operational questions into clear analytical tasks, assumptions, hypotheses, data requirements, and success criteria.

Develop decision-support analyses and performance insights across:

productivity, throughput, and capacity

quality, yield, and operational risk

delivery, shipment, and backlog performance

cost, margin, and resource drivers

manufacturing network and site performance

Support the definition and validation of KPI logic, baselines, targets, thresholds, and leading indicators, and help document how measures should be calculated and interpreted.

Apply data exploration, statistics, forecasting, diagnostic analytics, machine learning, and AI methods with guidance, selecting approaches that are appropriate for the business question.

Prepare clear visualisations, performance narratives, driver analyses, early-warning signals, and scenario implications that distinguish meaningful signals from noise and highlight areas requiring attention.

Build and test analytical prototypes using Python, SQL, R, BI tools, and cloud technologies; document methods, assumptions, limitations, and validation results to support reproducibility and responsible use.