Data Scientist, Integrated Business Planning

Job Description

Integrated Business Planning (IBP) brings together forecasting and supply 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 a centralized Planning Excellence Team (PET). This team will continue our Ignite IBP transformation journey which will strengthen our competitive advantage by enabling faster, more reliable planning. The

IBP Data Scientist role will bring key data scientist capabilities to the PET team. As the IBP Data Scientist, you will be responsible for supporting the fast-paced development and continued maintenance of demand, inventory, and supply planning models, refreshing 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 demand and supply planners, S&OE/S&OP processes, and enterprise decision-making
• Drive continuous improvement in analytics, data quality, and model reliability
• Drive automation and advanced analytics, leveraging Python, SQL, and BI tools to scale insights
• Partner with more senior team members to align analytics with standards, governance, and process maturity efforts
• Generate dashboards and KPIs that to support IBP governance and forum discussions
• Enable S&OE/S&OP with near-term supply-demand coverage analytics, including early warning indicators
  

Qualifications

• Bachelor's or Master's Degree or equivalent in data science, statistics, computer science, or related field.
• Post-graduate, certification, and/or license are a plus.
• Ability to work independently and as part of a team in a fast-paced environment.
  

Preferred skills include:

• Knowledge in predictive modeling, machine learning, and statistical analysis preferred.
• Programming languages such as Python, R, or SQL.
• Data visualization tools like Tableau, Power BI, Qlik, Spotfire or similar.
• Strong analytical and problem-solving skills with the ability to interpret complex data.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of advanced machine learning techniques and frameworks.