Ousmane Diagne

– Designed and deployed production-grade machine learning models and data pipelines to forecast renewable energy generation. – Developed site-specific and regional machine learning models for photovoltaic and wind energy production forecasting – Built machine learning and deep learning models for regional renewable energy forecasting – Developed data pipelines using Spark and Python on Azure Databricks

– Guided and supported students through their data science learning paths, providing mentorship on practical projects and real-world applications. Helped develop skills in statistical modeling, machine learning, and data analysis. – Mentored students throughout their data science curriculum – Assisted with project development, including statistical modeling, classification, scoring, and text and image analysis

– Developed data pipelines, KPIs, and dashboards to support fleet management and sustainability initiatives, with a focus on hybrid vehicle usage and environmental impact analysis. – Built data processing pipelines using Python and Databricks – Developed KPIs to monitor fleet performance and environmental metrics – Conducted studies on fleet greening and sustainability – Created interactive dashboards to visualize hybrid vehicle usage

– Developed data processing pipelines and interactive tools for biological experiment analysis, focusing on single-cell data and graph-oriented databases. – Developed R scripts for processing biological experiment data – Built R Shiny applications for single-cell data analysis – Implemented and tested a graph-oriented database using Neo4j

– Developed data-driven solutions to enhance banking transaction analysis and customer behavior insights. Built algorithms, APIs, and interactive tools to support decision-making and operational efficiency. – Implemented algorithms to categorize banking transactions based on description, amount, and date – Created behavioral banking indicators to profile customer activity – Developed Python scripts for data processing and transformation – Built APIs using Python and Flask to provide data access – Developed an R Shiny application to simulate approval or rejection of applications based on behavioral indicators

– Worked on developing end-to-end data solutions for smart energy management, focusing on predictive modeling, IoT data processing, and optimization of photovoltaic self-consumption. Contributed to building scalable data infrastructure and production-ready analytics tools. – Contributed to the modeling of a real-time data processing architecture for IoT data – Developed Big Data pipelines for data cleaning and aggregation using Python, Spark, and Azure Databricks – Developed predictive algorithms for electricity consumption and photovoltaic production forecasting – Developed optimization algorithms to maximize photovoltaic self-consumption – Built a simulation API for photovoltaic system performance using Python and Flask – Developed interactive visualization tools with R Shiny and Plotly – Deployed production-ready algorithms on Azure using Docker

– Delivered data science and business intelligence solutions for major clients in energy, aviation, and consumer goods sectors. – Developed algorithms to forecast energy consumption based on panel data (EDF R&D) – Built interactive visualization tools using R Shiny (EDF R&D) – Conducted a Proof of Concept (P.O.C.) to test a Big Data platform using Spark and Hive (EDF R&D) – Developed predictive models for product sales at Orly Airport (Aéroport de Paris) – Extracted and consolidated data from multiple sources into a unified database (Pernod Ricard) – Performed data cleaning and preprocessing (Pernod Ricard) – Created KPIs and visualizations to support internal audit decision-making (Pernod Ricard)

– Developed predictive scoring models leveraging classical and big data techniques to support a campaign management engine and improve proactive customer engagement.

– Cleaned and prepared databases – Performed exploratory statistical analysis – Calculated key performance indicators (KPIs) related to the economic activity of member companies

– Built and maintained a survey database (queries using Business Objects) – Processed and analyzed survey results – Drafted an evaluation report for the BAIP (Career Guidance Office) – Aggregated survey databases (VBA macros) – Assisted in data file validation for reporting results to the French Ministry of Higher Education and Research