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Open nowPosted 28 days ago

IBP Supply Planning Data Scientist

MyCareersFuture94,028 open roles

Pay
SGD 10,000 – SGD 15,000 a Monthly
Where
North, Singapore
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Your applicationOpen nowIBP Supply Planning Data ScientistMyCareersFuture · North, Singapore

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This job: posted 28 days ago

The posting

Job Description

Integrated Business Planning (IBP) brings together forecasting and planning across marketing, sales, finance, procurement, manufacturing, and customer service into a unified process. This integration ensures alignment across the enterprise, leading to superior customer outcomes.

Agilent is building our enterprise planning capabilities to drive IBP across the business by establishing Global Demand & Supply Planning Teams, and a centralized Planning Excellence Team (PET). Collectively, this new structure will continue to lead our Ignite IBP transformation journey which will strengthen our competitive advantage by enabling faster, more reliable planning. The IBP Supply Planning Data Scientist role will bring key data scientist capabilities to this effort.

As the IBP Supply Planning Data Scientist, you will be responsible for developing and maintaining advanced inventory and supply planning models, refreshing portfolio inventory targets, and monitoring key planning parameters to ensure the business is positioned to meet demand reliably and efficiently.

Principle Duties/Responsibilities:

  • Provide data-driven insights, dashboards, and scenario modeling to support supply planners, S&OE/S&OP processes, and enterprise decision-making
  • 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 Planning Stewards to align analytics with standards, governance, and process maturity efforts
  • Build, maintain, and enhance inventory optimization models (safety stock, segmentation, variability-based rules, reorder logic)
  • Refresh inventory targets across the portfolio and ensure alignment with service expectations, demand patterns, and supply constraints
  • Monitor and tune critical inventory parameters (lead times, variability, cycle stock inputs, yield factors) based on actual system performance
  • Analyze inventory health (target adherence, shortages, excess, age, replenishment performance) and provide insights to planners and leaders
  • Provide inventory-focused scenario analysis for demand changes, supply disruptions, or service-level shifts
  • Support supply planners with modeling supply capability (RCCP, constraints, capacity modeling, yield/throughput assumptions)
  • Generate dashboards and KPIs that reflect supply plan stability, attainment, constraint exposure, and readiness
  • Enable S&OE/S&OP with near-term supply-demand coverage analytics, including early warning indicators
  • Develop and maintain supply-focused models (scenario models, coverage calculators, constraint dashboards) for decision support

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.
  • 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.

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.
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