
About RetSci
[ Important: Please send us your resume in English only ]
About our team
At Restci, we look for new and creative ways to help our customers optimize their inventory and sales processes, or refine their order fulfillment strategies.
About this Internship
We’re looking for highly talented and motivated students to join us as interns within our Data Science Consulting team and work on the following projects:
Project 1: LLM-Powered Document Retrieval Chatbot for Google Drive
We aim to develop a fully free, AI-powered chatbot capable of searching and retrieving information from our extensive documentation stored on Google Drive. Using open-source tools and models, the chatbot will be able to answer user queries by locating relevant content across our files, summarizing the information in a clear response.
The solution will leverage free models like Meta’s Llama 2 or Mistral 7B, alongside LangChain for easy integration and document management and open-source vector database and search tools like FAISS for efficient retrieval.
The project offers the opportunity to build a sophisticated document-querying chatbot using cutting-edge, cost-effective AI technologies and to explore data organization and retrieval mechanisms within a real-world business context.
Project 2 : Forecast Assessment for Inventory Optimization
Effective inventory management is crucial to meet customer demand while minimizing costs.
Forecasting plays a pivotal role in this process by predicting future demand, enabling businesses to optimize their inventory levels and replenishment strategies.
Accurate forecasts provide the foundation for setting key Inventory Replenishment Parameters. These parameters are what determines the optimal inventory levels ensuring that stock levels are maintained efficiently without overstocking or stockouts.
This project aims to assess how the forecast perfomance is impacting the inventory optimzation by analyzing multiple forecast scenerios issues and model their impact. And eventually the goal is to determine the right forecast adjustements to improve inventory optimization results by computing ROI metrics and simulate the impact based on different forecasts feed.
Through this analysis, we will explore the balance between demand forecasting performance and inventory optimization results in order to come up with the right forecast adjustments which will lead to reaching the right inventory levels and replenishment strategies.
Project 3: PRISM: Enhancing Retail Shrinkage Sensitivity Analysis with Generative AI Synthetic Data Generation
The project aims to enhance sensitivity analysis for retail shrinkage by addressing the lack of data in terms of quantity, diversity, and unseen but foreseeable scenarios. Retail sensitivity models often face challenges such as overfitting and unreliable predictions due to limited or heterogenous datasets. To overcome these challenges, the project will leverage generative AI for the following purposes:
– The first objective is to use synthetic data generation to create diverse and realistic datasets. This will introduce plausible variations of key features such as crime rates, customer demographics, new stores, allowing the model to be trained on a broader range of conditions. By doing so, the project will ensure that the model can make more robust predictions, even in the face of limited or biased historical data.
– The second objective is to apply counterfactual analysis to simulate plausible « what-if » scenarios, such as shifts in consumer behavior, economic changes, or future crime patterns that have not yet occurred but are foreseeable. By generating and exploring these future-like conditions, the project will enable retailers to anticipate how shrinkage might evolve under different circumstances.
We would love to hear from you if
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