Yohan – Python, SQL, Microsoft Power BI

Modernization and scaling of a climate data analysis and reporting platform assessing French companies' current and projected carbon emissions. The platform processes data from approximately 10,000 companies annually, supporting a workflow involving 100+ analysts and sector experts. Led the modernization of a multi-user Excel/VBA/Access application and developed Python-based calculation, data quality, and reporting pipelines, enabling more reliable climate indicators and substantially richer automated reports.

• Designed, developed and maintained a modular end-to-end Python data pipeline for multi-source ingestion, data quality control, metric computation and dynamic report generation.
• Completely redesigned and maintained a critical multi-user Excel/Access VBA application with relational database redesign, data and code quality improvements, configuration externalisation, security hardening and network performance optimisation through caching.
• Designed, created and maintained around fifteen complex Excel questionnaires integrated with a VBA application.
• Served as technical lead for a team of Python/VBA developers and testers, providing technical guidance, design decisions, prioritisation, process simplification and rapid prototyping.
• Worked closely with business teams to gather requirements, frame problems and design solutions.
• Performed exploratory data analysis and prototyping.

Led data analysis, reporting, and automation projects across multiple business departments, including accounting, fraud prevention, and risk management. Consolidated financial and operational data from 35 regional offices, delivering approximately 30 documented tools and applications over two years. Managed projects end-to-end, from business requirements and prioritization to development and deployment.

·       Automated national fraud prevention reporting, reducing preparation time from several weeks to approximately one day and enabling a transition from annual to quarterly and monthly reporting.

·       Introduced Power BI analytics and visualizations, helping management identify risk patterns, explore correlations, and communicate findings more effectively.

·       Built a national risk reporting pipeline, consolidating indicators from 35 regional offices and automating risk assessments and reporting.

·       Developed statistical methods for KPI reliability, using sample-based controls and 95% confidence intervals to quantify uncertainty and validate reported indicators.

·       Designed data collection and validation tools for regional offices, improving data quality and consistency across national risk reporting.

·       Developed a process mapping system, tracking organizational processes over time and linking them to risk assessments.

·       Automated accounting discrepancy analysis, reducing processing time from approximately two weeks to 1–2 days through rule-based transaction matching, anomaly detection, and suggested corrections.

Developed automated data collection and matching solutions to support targeted commercial prospecting in the healthcare and aesthetics sectors. Replaced manual searches with structured workflows delivering prospect lists of typically 2,000–5,000 healthcare professionals per assignment, selected according to their specialties and expertise.

·       Source research and assessment: identified relevant online directories and data sources, evaluating their coverage and suitability for specific targeting requirements.

·       Web scraping and data extraction: developed VBA and Python scripts to collect healthcare professional information at scale, replacing manual prospect identification.

·       Data cleaning and entity matching: standardized and structured extracted data, reconciled records across sources, and applied matching and qualification rules based on professional specialties.