Posted May 7, 2026
Key Responsibilities:
Develop and deploy time-series forecasting models including ARIMA, Prophet, XGBoost, and Deep Learning. - Design demand forecasting solutions for supply chain, inventory, and sales planning. - Enhance forecast accuracy through feature engineering, external signals, and ML optimization techniques. - Collaborate with supply chain, operations, and business stakeholders to align forecasting outputs with business requirements. - Analyze demand variability, seasonality, and trends to support inventory optimization and production planning. - Translate complex models into business-friendly insights and dashboards. - Work with large-scale datasets using platforms such as Databricks for ML pipelines and distributed processing, Palantir for data integration and modeling, and Snowflake for data warehousing. - Build and maintain data pipelines and feature stores while ensuring data quality, governance, and scalability. - Deploy models into production environments using CI/CD and MLOps practices. - Monitor model performance, continuously improve based on feedback loops, and automate workflows for real-time or near-real-time forecasting. Qualification Required:
Experience with Databricks (Highly Preferred), Palantir Foundry (Preferred), and Snowflake (Data Lake/Warehouse experience). - Experience in building end-to-end ML pipelines and exposure to data lake architectures and big data frameworks (Spark, Delta Lake). - Knowledge of forecasting at scale in CPG, retail, or manufacturing industries. (Note: Any additional details of the company were not provided in the job description.) Role Overview: You will be joining an advanced analytics team as a Senior Data Scientist specializing in Demand Forecasting and Supply Chain Analytics. Your primary focus will be on blending machine learning, forecasting, and large-scale data engineering to drive business-critical decisions across supply chain and operations. You will work on building scalable forecasting models and leveraging data platforms such as Palantir, Databricks, and Snowflake to translate data into actionable insights that enhance planning accuracy and operational efficiency. Key Responsibilities:
Develop and deploy time-series forecasting models including ARIMA, Prophet, XGBoost, and Deep Learning. - Design demand forecasting solutions for supply chain, inventory, and sales planning. - Enhance forecast accuracy through feature engineering, external signals, and ML optimization techniques. - Collaborate with supply chain, operations, and business stakeholders to align forecasting outputs with business requirements. - Analyze demand variability, seasonality, and trends to support inventory optimization and production planning. - Translate complex models into business-friendly insights and dashboards. - Work with large-scale datasets using platforms such as Databricks for ML pipelines and distributed processing, Palantir for data integration and modeling, and Snowflake for data warehousing. - Build and maintain data pipelines and feature stores while ensuring data quality, governance, and scalability. - Deploy models into production environments using CI/CD and MLOps practices. - Monitor model performance, continuously improve based on feedback loops, and automate workflows for real-time or near-real-time forecasting. Qualification Required:
Experience with Databricks (Highly Preferred), Palantir Foundry (Preferred), and Snowflake (Data Lake/Warehouse experience). - Experience in building end-to-end ML pipelines and exposure to data lake architectures and big data frameworks (Spark, Delta Lake). - Knowledge of forecasting at scale in CPG, retail, or manufacturing industries. (Note: Any additional details of the company were not provided in the job description.)
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