Supply Chain Data Scientist

Job Title

Supply Chain Data Scientist

Experience

58+ Years

Job Summary

We are looking for a highly skilled Supply Chain Data Scientist with strong experience in applying Data Science, AI, Machine Learning, Advanced Analytics, and Statistical Modelling to solve real-world supply chain challenges.

Supply Chain domain experience is mandatory for this role. The ideal candidate must have hands-on experience working with supply chain data, processes, and business use cases such as demand forecasting, inventory optimization, replenishment, logistics optimization, supplier analytics, and supply chain planning.

The candidate will work closely with Supply Chain, Operations, Procurement, Logistics, and IT teams to develop data-driven and AI-powered solutions that improve business decision-making and end-to-end supply chain performance.

Key Responsibilities

• Develop and deploy AI/ML predictive and prescriptive models for supply chain use cases.
• Build solutions for demand forecasting, inventory optimization, replenishment, capacity planning, and supply planning.
• Develop analytics models for supplier risk analysis, logistics optimization, network design, and margin analysis.
• Analyze large and complex datasets from ERP systems, planning tools, logistics platforms, and external data sources.
• Identify patterns, risks, inefficiencies, and business opportunities using advanced analytics.
• Build scenario simulations and what-if analysis models to support supply chain decision-making.
• Apply machine learning, statistical modelling, time-series analysis, clustering, regression, and optimization techniques.
• Improve forecast accuracy, inventory availability, service levels, and operational efficiency.
• Translate complex supply chain business problems into scalable analytical and AI solutions.
• Develop and productionize machine learning models.
• Continuously monitor and improve model performance based on changing business conditions and stakeholder feedback.
• Collaborate with Supply Chain, Operations, Procurement, Logistics, and IT stakeholders.
• Support deployment, monitoring, and lifecycle management of AI/ML models using MLOps practices.

Mandatory Skills

Data Science & Machine Learning

• Strong experience in Data Science and Machine Learning.
• Experience developing predictive and prescriptive analytics solutions.
• Strong knowledge of:
• Regression
• Time Series Forecasting
• Clustering
• Optimization Techniques
• Statistical Modelling

Supply Chain – Mandatory

Candidates must have hands-on Supply Chain domain experience and exposure to one or more of the following:

• Demand Forecasting
• Supply Planning
• Inventory Optimization
• Replenishment Planning
• Capacity Planning
• Procurement Analytics
• Supplier Risk Analytics
• Logistics Optimization
• Transportation Analytics
• Supply Chain Network Design
• End-to-End Supply Chain Visibility

Technical Skills

• Strong proficiency in Python or R.
• Strong SQL skills.
• Experience working with large and complex datasets.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Exposure to MLOps and ML model deployment.
• Experience with data visualization tools such as:
• Power BI
• Tableau
• Similar BI/Visualization tools

Preferred Skills

• Experience working with ERP and Supply Chain Planning systems.
• Experience with SAP, Oracle, Kinaxis, Blue Yonder, or similar supply chain platforms.
• Experience building forecasting or optimization models in a production environment.
• Knowledge of cloud-based data and analytics platforms.
• Experience with model monitoring and continuous model improvement.
• Strong business understanding of Supply Chain and Operations.

Candidate Profile

• 5–8+ years of experience in Data Science, AI, Machine Learning, or Advanced Analytics.
• Supply Chain domain experience is mandatory.
• Strong hands-on experience working with real-world supply chain datasets and planning processes.
• Ability to communicate analytical insights to both technical and business stakeholders.
• Experience taking Data Science or ML models from development to production is preferred.