Job Description
Role Summary:
We are seeking a data-driven Demand Planner who thinks like a Data Scientist. Instead of relying solely on manual spreadsheets, you will leverage advanced analytics, machine learning, and automation to revolutionize our global forecasting accuracy. You will build the bridge between raw data and actionable supply chain strategies.
Key Responsibilities
Advanced Forecasting Models:
- Develop/maintain predictive models using Python, R or machine learning to forecast global demand with higher accuracy.
- Move beyond simple moving averages; implement advanced time-series algorithms (e.g., ARIMA, Prophet, XGBoost, LSTM) to capture seasonality and market trends.
- Data Pipeline & Automation:
- Design and manage ETL processes (Extract, Transform, Load) to consolidate data from ERP (SAP/Oracle), CRM, and market intelligence sources.
- Use SQL to query large datasets and automate the data cleaning process, reducing manual Excel work for the broader team.
- Scenario Planning & Simulation:
- Perform A/B testing on forecast models to continuously improve performance.
- Business Insights Visualization):
- Create dynamic dashboards (PowerBI) that visualize forecast confidence intervals and risks for senior management.
Translate complex data findings into clear business narratives for sales and marketing teams.
- Education: BS/MS in Statistics, Data Science, Computer Science, Economics, or Supply Chain Management.
- Technical Skills (Must-Haves):
- Proficiency in Python (Pandas, Scikit-learn, NumPy) or R for data modeling.
- Strong SQL skills for complex database queries.
- Experience with Time Series Analysis and statistical forecasting methods.
- Domain Knowledge:
- 2+ years of experience in Demand Planning, Supply Chain, or a Data Analyst role within a retail/manufacturing context.
- Understanding of inventory management concepts (Safety Stock, DOI, Lead Time).
- Soft Skills:
- Ability to explain technical models and work with IT& non-technical stakeholders.
Proactive problem solver with a "builder" mindset.
Preferred
ü Experience with cloud platforms (AWS, Google Cloud, Azure) for deploying models.
ü Familiarity with deep learning libraries (TensorFlow/PyTorch) is a plus.
