Role Overview
We are seeking a Lead Forecasting & AI Architect to design and lead the
development of an AI-driven Sales and Procurement Forecasting platform on Azure.
This role will define forecasting strategy model architecture blending logic (AI
Sales inputs) and ensure production-grade deployment through Azure MLOps.
This is a hands-on leadership role - not advisory.
Key Responsibilities
Design end-to-end SKU-level forecasting architecture
Build and validate multi-model forecasting systems
Develop hybrid models combining:
o Historical demand patterns
o Sales pipeline intelligence
o Procurement signals
Define forecast reconciliation:
o SKU Product Line Business Unit
Implement probabilistic forecasting (P10 / P50 / P90)
Establish model governance explainability and KPI tracking
Define Cost of Forecast Error (CoFE) optimization approach
Lead Azure MLOps strategy (training monitoring retraining)
Partner with Sales Procurement and Finance stakeholders
Required Technical Skills
Forecasting & ML Expertise
8 years in predictive analytics / forecasting
Strong expertise in:
o Time-series forecasting (ARIMA Prophet LSTM XGBoost etc.)
o Hierarchical forecasting
o Intermittent demand modeling
o Ensemble / model blending
Experience building:
o Sales forecasting systems
o Procurement / supply forecasting systems
Experience with probabilistic forecasting
Advanced feature engineering for demand signals
Backtesting frameworks & rolling forecast validation
Forecast KPI design:
o MAPE
o Bias
o Forecast churn
o Cost of Forecast Error
Azure & MLOps
Azure Machine Learning (pipelines model registry batch scoring)
Azure ML Studio / SDK
MLflow or equivalent tracking
Azure DevOps / CI-CD pipelines
Data versioning & model governance
Drift detection & retraining automation
Preferred Qualifications
Experience in semiconductor / manufacturing forecasting
Exposure to S&OP / IBP processes
Experience building explainable AI dashboards
Knowledge of inventory optimization models
What Success Looks Like (First 6 Months)
SKU-level baseline forecast deployed
Hybrid AI Sales model live
Forecast accuracy improved on high-impact SKUs
Model retraining automated
Executive KPI pack generated automatically
Role Overview We are seeking a Lead Forecasting & AI Architect to design and lead the development of an AI-driven Sales and Procurement Forecasting platform on Azure. This role will define forecasting strategy model architecture blending logic (AI Sales inputs) and ensure production-grade deploymen...
Role Overview
We are seeking a Lead Forecasting & AI Architect to design and lead the
development of an AI-driven Sales and Procurement Forecasting platform on Azure.
This role will define forecasting strategy model architecture blending logic (AI
Sales inputs) and ensure production-grade deployment through Azure MLOps.
This is a hands-on leadership role - not advisory.
Key Responsibilities
Design end-to-end SKU-level forecasting architecture
Build and validate multi-model forecasting systems
Develop hybrid models combining:
o Historical demand patterns
o Sales pipeline intelligence
o Procurement signals
Define forecast reconciliation:
o SKU Product Line Business Unit
Implement probabilistic forecasting (P10 / P50 / P90)
Establish model governance explainability and KPI tracking
Define Cost of Forecast Error (CoFE) optimization approach
Lead Azure MLOps strategy (training monitoring retraining)
Partner with Sales Procurement and Finance stakeholders
Required Technical Skills
Forecasting & ML Expertise
8 years in predictive analytics / forecasting
Strong expertise in:
o Time-series forecasting (ARIMA Prophet LSTM XGBoost etc.)
o Hierarchical forecasting
o Intermittent demand modeling
o Ensemble / model blending
Experience building:
o Sales forecasting systems
o Procurement / supply forecasting systems
Experience with probabilistic forecasting
Advanced feature engineering for demand signals
Backtesting frameworks & rolling forecast validation
Forecast KPI design:
o MAPE
o Bias
o Forecast churn
o Cost of Forecast Error
Azure & MLOps
Azure Machine Learning (pipelines model registry batch scoring)
Azure ML Studio / SDK
MLflow or equivalent tracking
Azure DevOps / CI-CD pipelines
Data versioning & model governance
Drift detection & retraining automation
Preferred Qualifications
Experience in semiconductor / manufacturing forecasting
Exposure to S&OP / IBP processes
Experience building explainable AI dashboards
Knowledge of inventory optimization models
What Success Looks Like (First 6 Months)
SKU-level baseline forecast deployed
Hybrid AI Sales model live
Forecast accuracy improved on high-impact SKUs
Model retraining automated
Executive KPI pack generated automatically
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