PFE 2027 RetSci – Smarter Oracle Forecasting: Parameter Tuning by Product Behavior

Sujet 2 de l'annonce RetSci « Data Science Consultant Intern - PFE 2027 » (offre ouverte, Tunis ou Sfax, hybride ; CV en anglais ; une candidature unique pour l'annonce, sujet non garanti). Oracle Retail AI Foundation (AIF) offers many forecasting parameters, but today the same generic settings are applied to every product group, from fast-moving basics to slow-selling fashion items. You will build a data-driven method to find the best parameters for each type of product and validate it directly in Oracle's latest demo environment. Your results will feed into real client implementations. You will build it with AI tools (Claude, Cursor, Databricks).

Product characterization framework grouping items by demand behavior (velocity, seasonality, volatility, lifecycle) ; Tuning pipeline in Databricks to test parameter combinations for each product group ; Parameter matrix mapping each product profile to recommended AIF settings ; Validation in the Oracle test environment, comparing accuracy against current generic settings ; Playbook for reusing the matrix on new client implementations ; Final presentation of results and recommendations