Experience Level: 46 Years
Employment Type: Full-Time
Department: AI Platform / Digital Experience Innovation
Core Tech Stack: Python, XGBoost, Random Forest, Ensemble Methods, Oracle ERP, SQL

Key Responsibilities

Pipeline Development: Design, build, and maintain robust, automated multi-model machine learning pipelines (including XGBoost, Random Forest, and custom Ensemble architectures) for high-accuracy demand and sales forecasting.
Granular Modeling: Develop and fine-tune predictive models at both macro-levels (PPGC) and highly granular levels (SKU) to support localized and global business planning.
Data Engineering & Preprocessing: Architect data preprocessing workflows, handling missing values, scaling, feature engineering, and outlier detection from enterprise data sources.
System Integration: Collaborate with IT and enterprise data teams to orchestrate seamless data integration pipelines between the ML forecasting platform and Oracle ERP systems.
Accuracy & Validation: Continuously measure, track, and optimize model performance using robust metrics, specifically targeting MAPE (Mean Absolute Percentage Error) and minimizing forecast bias.
Workflow Design: Conceptualize and implement Business Unit (BU) override workflow systems, enabling domain experts to review, validate, and inject manual adjustments safely into automated model outputs.

Required Skills & Qualifications

Experience: 46 years of professional experience as an AI/ML Engineer, Data Scientist, or Predictive Analytics Engineer with a primary focus on time-series forecasting or demand planning.
Core ML Expertise: Advanced, hands-on proficiency in building tree-based models (XGBoost, Random Forest) and implementing Ensemble methods.
Programming: Mastery of Python and its scientific/ML ecosystem (e.g., Pandas, NumPy, Scikit-Learn, Statsmodels).
Enterprise ERP Knowledge: Proven track record of integrating machine learning models or pipeline outputs with Oracle ERP or similar enterprise-scale resource planning software.
Domain Knowledge: Solid understanding of supply chain, inventory metrics, SKU management, and corporate sales cycles.
Education: Bachelors or Masters degree in Computer Science, Data Science, Statistics, Operations Research, or a related quantitative field.

Preferred Skills

Exposure to MLOps frameworks for pipeline automation, model deployment, and version control (e.g., MLflow, Kubeflow, Git).
Strong SQL skills for querying massive, complex relational databases.
Experience working in high-tech manufacturing, semiconductor, or complex hardware supply chain environments.

AI/ML Engineer