Overall mission: Development of data and AI tools to optimize business processes within the Innovation Unit.

Tasks:

Developed R Shiny applications for risk analysis, CRC management, and automation of permanent files.

Built AI assistants for event management and environmental & social screening.

Deployed LLMs both in the cloud and on-premise.

Automated processes and improved document management/. Enabled adoption of secure AI solutions.

Overall mission: Implementation of Data Products and a Data Catalog for OWF and Group Finance.

Tasks:

Defined business requirements in collaboration with OWF and Finance teams.

Developed data ingestion and transformation pipelines.

Provided technical support and maintenance for multi-entity data science teams.

Delivered several data products, referenced and accessible through the catalog.

Developed advanced algorithms to calculate wind speed, wind direction, and atmospheric pressure, contributing to the efficiency of renewable energies.

Deployed and monitored machine learning models to enhance the performance of wind and solar farms.

Conducted in-depth analysis of performance anomalies in these facilities, devising innovative solutions to optimize clean energy production.

Implemented data quality and data governance frameworks to ensure result reliability.

Orchestrated releases of packages and executed unit and integration tests to ensure solution robustness.

Created connectors to Azure Data Explorer (ADX) clusters for streamlined data access.

Built dashboards and alert systems using Grafana to real-time monitor energy park performance.

Leveraged a wide range of tools and technologies, including Python, Spark, R, Git, Docker, Jenkins, Artifactory, and platforms such as Azure Storage and Databricks.

Achieved a 5% increase in energy production through anomaly detection and predictive maintenance models.

Design and development of a centralized data platform encompassing four major clinical concepts (symptoms, diagnosis, causes, and treatments) to enhance real-world data quality and optimize the generation of valuable insights.

Implementation of an epidemiological data center covering oncology, neuroscience, and rare diseases, enabling comprehensive disease monitoring and fostering targeted treatment development strategies.

Drafting technical project documentation and optimizing the developed code to ensure efficient execution.

Implemented the solution as the company's main knowledge center.

POC to help with decision-making based on the analysis of emotions during an interview.

Implement real-time scraping of LinkedIn data to enrich contextual information.

Production of deep learning models that detect emotions and facial attributes in the context of an interview.

A toolbox of deep neural networks designed for HR to analyze emotions via facial expression, connecting images and HR description of the Person and classifying the profiles.

Develop modern, efficient and maintainable models for a wide range of crwodfunding processes for SMEs / VSEs around the world using ML & DL models.

Work with a variety of different data : sturctured and textual data.

Develop Scrapping algorithms to collect various types of data from the web.

Communicate with multi-disciplinary teams of engineers, designers, testers, and HR on a daily basis.

Implement the solutions as AI backend for a FinTech prototype.