Sr. Data Scientist (Forecasting)

What you’ll do: Design, develop, test, and deploy statistical and machine learning forecasting models in production on cloud-based analytics platforms, emphasizing demand pattern recognition, algorithm selection, outlier correction, and parameter optimization to reduce bias and improve forecast accuracy. Build and maintain structured, programmatic forecasting pipelines that run at scale, usingML Opspractices to automate weekly forecast generation and reduce manual effort.

Embed: Agentic AI and GenAIinto forecasting workflows to identify opportunities to improve accuracy, automate root cause analysis, and support insights generation and decision-making. Continuously evaluate and apply emerging AI/ML methods, includingdeep learningandprobabilistic forecasting, to increase accuracy and scalability across a complex portfolio ofSKUs, clusters, and data sources .Act as a liaison between technical and functional teams by partnering with Supply Chain Planning leads and collaborating with stakeholders across Marketing, Finance, and Salesto connect business needs with system and process solutions. Contribute toEnterprise Forecastingas part of the One Planninginitiative and support Customer Order Fulfilmentimprovements with standardized, scalable, cross-functional forecasting solutions. Deliver training and forecast office hours to Supply Chain Planning users to build analytics literacy, increase adoption, and strengthen enterprise value. Create and enhance Power BIreports for forecast KPI monitoring and data validation, and present findings using data visualization and PowerPointtailored to the audience.

Requirements: Bachelor’s degreein statistics, mathematics, economics, business, or a related field.5+ yearsof experience in data science, forecasting, or advanced analytics, including hands‑on experience withSQL , Python, or a similar language.5+ yearsextracting, transforming, and analyzing large datasets, plus experience creating data visualizations to communicate insights.3+ yearsdeploying and maintaining machine learning models in production, including regression, classification, time series modeling, and feature engineering. Tools and technologiesSQL, Python, Machine learning, Power BI, PowerPointML Ops, Databricks, Snowflake, Microsoft AzureGenAI, Agentic AI, deep learning, probabilistic forecasting