Paris, Île-de-France, France
• Conducted an in-depth literature review on the correlation between RNA sequences and various diseases, highlighting how state-of-the-art deep learning models and sequencing techniques are leveraged to optimize disease detection. • Designed and implemented a robust pipeline using Nextflow and Python to streamline RNA-seq data collection, processing, and quality control, ensuring high data quality and reliability for the subsequent analyses, providing confidence in the results and enabling accurate interpretation of gene expression patterns. • Integrated denoising autoencoder models to enhance the precision and depth of bioinformatics and biostatistics analyses, reducing noise and simplifying complex gene expression data. • Optimized French healthcare text anonymization by fine-tuning Named Entity Recognition (NER) models and integrating NLP with regex techniques, achieving 80% accuracy and processing speeds 10-15 times faster than existing tools, ensuring compliance with GDPR
• Performed in-depth analysis of business requirements, designed robust data models, and developed efficient data extraction and reporting workflows to deliver interactive dashboards. • Designed and implemented a robust ETL pipeline to ensure secure and reliable data transmission and processing. • Created an advanced dashboard tracking system using the Robot Framework to systematically monitor and update the refresh dates of all dashboards developed by the data team.
• Co-developed an interactive dashboard with senior developers, enhancing user experience through improved data visualization, leading to a more intuitive user interface. • Implemented A/B testing for key website features to optimize user engagement and conversion rates, contributing to data-driven design decisions. • Assisted in front-end development, utilizing HTML, CSS, and JavaScript to maintain and improve website functionality and responsiveness across devices.
• Extracted and digitized data from over 3000 paper records covering a decade (2009-2019) to build a comprehensive dataset. • Utilized SQL and Python (Pandas, NumPy) to analyse accident-prone routes, mortality rates, and vehicle fire risks post-collision. Employed Tableau and Matplotlib for effective data visualization, highlighting critical safety insights. • Derived actionable insights from complex data, informing safety initiatives and preventive measures to enhance road safety and vehicle standards.