Data Scientist

Role & responsibilities

• Demand forecasting and planning: Build and improve daily and monthly demand forecasts across warehouses, vans, channels and SKUs. Apply time series models, hierarchical reconciliation and forecast evaluation to support sales, replacement and production planning.
• Machine learning deployment and automation: Develop and maintain production ML pipelines using Python, Ray and Azure ML. Automate data preparation, training, inference and forecast delivery; monitor accuracy, runtime and reliability.
• Data analysis and ETL: Extract, clean, transform and load sales data from SQL Server, MySQL and other sources. Build reliable ETL pipelines, resolve data quality issues, analyze trends, and deliver dashboards to support forecasting and business decisions.
• Sales and operational optimization: Develop Must Visit recommendations, outlet visit frequency and sequencing logic, and statistical shelf size and safety stock recommendations. Work with sales, operations, and technology teams to validate results and integrate solutions into business applications.

Document extraction and AI: Develop document processing solutions using OCR and Vertex AI to extract, structure and validate information from business documents and integrate it into downstream workflows.