Lamarana Diallo

• Extract, cleanse and transform daily stock data from two systems: WMS (WES) and ERP (Cegid ORLI) • Compare and analyze discrepancies in stock levels between systems to ensure accuracy and traceability • Design and automate dashboards (Metabase) to monitor stock KPIs by boutique and warehouse • Create materialized views in PostgreSQL to improve performance and reduce dashboard load times • Build a scalable data model to support stock valuation, movement tracking, and anomaly detection • Propose new KPIs and stock analysis methodologies to better support business operations • Assist in the setup of future ETL workflows (Airbyte) and client-level access restrictions in dashboards

• Extract, cleanse and transform data from multiple sources (SQL, BigQuery, Snowflake, APIs) • Ensure data quality and reliability by detecting anomalies and proposing corrections • Design and automate dashboards and reports (via Power BI) to monitor KPIs • Set up optimized data pipelines to improve query and dashboard performance • Automation of repetitive processes with Python scripts

- Developed predictive models for karst hydrology using machine learning techniques - Processed and integrated complex hydrological data from multiple sources, creating a unified database for analysis - Collaborated with researchers to interpret results and present findings to stakeholders

- Statistical analysis and segmentation of the customer base, increasing the effectiveness of targeted marketing. - Creation of interactive dashboards using Metabase, providing real-time information on sales performance and customer behavior. - Implementation of A/B testing methodologies, resulting in improved conversion rates.

-Developed R packages for fish capture modeling, enhancing research capabilities in marine biology - Performed exploratory data analysis and advanced modeling on ecological datasets - Presented findings at departmental seminars, receiving commendation for innovative approach

- Identification of sources and patient data collection - Cleaning data to make it usable - In-depth analysis to assess risk factors associated with cardiovascular disease - Application of Cox model to analyze patient survival time - Presentation of results at academic meetings and seminars - In-depth interpretation of results to establish significant correlations between variables and incidences of cardiovascular disease