Mohammed Jeddi

✔Designed and implemented machine learning/deep learning models for various use cases: → Sales forecasting. →Product embeddings. → Inventory allocation optimization. →Data analysis for steering committees. ✔ Developed a supply chain anomaly detection system using LLMs. ✔ Built a predictive model for product component valuation (e.g., RTX4090). ✔ Developed algorithms and pipelines in Dataiku DSS & Databricks. ✔ Implementation of a Git workflow for modularization and versioning of data science projects. ✔ Created dynamic dashboards for stakeholders. ✔ Managed data science projects (defining objectives, governance, deliverables). ✔ Facilitated workshops to promote data science concepts. ✔ Translated business needs into data science solutions. 💼Tools : Time Series Forecasting, Clustering, Embeddings, Data Analytics, Operation Research, LLMs, Langchain, Python, Pyspark, DSS, Databricks, Power BI 🔊Languages : English

✔Assisted IT and finance teams with data management and process improvement. ✔Improved IT systems reliability by: → Updating databases. → Contributing to IT process definitions. → Creating user guides. → Analyzing user feedback. → Developing specifications for IT tool improvements. ✔Helped define IT architecture. ✔Created a Q&A chatbot and contract reader. ✔Improved forecasting models for financial data. 💼 Tools: Time Series Forecasting, NLP, Data Analytics, Power BI, Python, R, 🔊Languages : English

✔Managed multi-year purchasing partnerships for strategic group needs. ✔Conducted data cleansing, exploration, and analysis of historical data for commercial negotiations. ✔Designed new segmentation of purchasing categories using clustering models. ✔Implemented proactive contract administration system with digital alerts. ✔Prepared regular purchasing activity reports. ✔Supported digital procurement transformation projects. 💼Tools: MACHINE LEARNING, Project Management, Data Analysis, R, Python, VBA- EXCEL & PowerBI. 🔊Languages English, French

📝Subject: « Improving the performance of grinding and settling process» ✔Analysis of KPIs for the grinding plant using statistical tests. ✔Analyze gaps and set up models for problem resolution allowing to take out the levers of improvement for the grinding process. ✔Improvement of additive consumption at the settling level through the study of correlations between inputs, parameterization, and process outputs, the design of supervised models to predict the level of consumption and the creation of a consumption standard via meta-heuristics. Estimated gain of 100,000 Euros. ✔Design of an Rshiny application that consolidates all results. 💼Tools: MACHINE LEARNING, Operation research, Project Management, R & Python, RShiny