
Chief Demand Planning Specialist
2 days ago
Please find below the key requirements and responsibilities for this role.
We are at the forefront of innovation in supply chain planning, leveraging AI-driven demand planning and inventory replenishment software to disrupt markets. Our goal is to provide solutions that empower small and medium-sized businesses (SMBs) with technological advantages previously exclusive to large enterprise companies.
This focus has become increasingly pertinent in light of supply chain disruptions caused by COVID-19. SMBs now have access to technological advancements previously reserved for large corporations like Walmart, Amazon, Lowe's, and Home Depot, who have invested hundreds of millions into these technologies.
**Job Overview**: In this role, you will design, implement, and deploy a next-generation platform tightly integrated with Amazon AWS services such as EMR, Athena, Glue, and Spark. You will develop an ultra-real-time, AI-driven demand planning engine to support industries managing perishable items, including food manufacturing, restaurants, grocery stores, pharmaceuticals, and more.
You will also modernize our demand forecasting capabilities through an enterprise-grade ML platform. Your role will blend hands-on machine learning with solid software engineering practices to deliver robust, scalable, and transparent forecasting capabilities.
**Key Responsibilities:**
- Design and deploy production-grade forecasting models
- Develop end-to-end machine learning pipelines for data ingestion, feature engineering, model training, and deployment
- Build probabilistic models to quantify forecast uncertainty
- Write clean, modular, object-oriented code following best practices
- Implement MLOps best practices including model versioning, monitoring, and CI/CD
- Work with distributed training frameworks for scalable model development
- Collaborate with cross-functional teams (engineering, product, strategy) to align solutions with business needs
- Work with cloud-based environments (e.g., Databricks, AWS, GCP) for large-scale data processing
Requirements:
- Experience in demand forecasting or retail/supply chain
- Exposure to containerization (Docker, Kubernetes)
- Familiarity with data orchestration frameworks (Airflow, Prefect)
- Experience with explainable AI frameworks
Preferred Qualifications:
- 3–6 years of relevant experience in Data Science / ML Engineering
- Strong proficiency in Python and core libraries such as pandas, numpy, scikit-learn
- Hands-on experience with deep learning frameworks (preferably PyTorch)
- Experience with distributed training and large-scale datasets
- Proven ability to build scalable, production-grade ML pipelines
- Familiarity with MLOps practices (CI/CD, monitoring, retraining pipelines)
- Solid understanding of statistical modeling, regression, and time series
- Experience working in cloud environments (Databricks, AWS, GCP)
- Strong software engineering skills with a focus on clean, modular, object-oriented code
- Excellent problem-solving, communication, and collaboration skills
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