I'm a quantitative Data Analyst & Scientist with a strong background in statistical modeling, data pipeline engineering, and credit risk modeling. Holding a Master's in Applied Econometrics & Statistics from the University of Orléans (France) and an engineering degree from ENSAE Dakar, I combine rigorous analytical thinking with a passion for building reliable, production-ready data solutions.

Sept 2023 – Jan 2026

• Automated data pipelines for trade data processing: quality control, cleaning, standardization, and indicator production
• Designed visualizations and quality monitoring dashboards for trade flow analysis
• Stored, consolidated, and made data available in a SQL Server data warehouse for reporting and analytics
• Migrated SAS codebases to Python to industrialize analytical processes; documented business rules and processes
• Leveraged data to produce analytical and thematic reports for internal teams and institutional partners
• Delivered targeted training sessions on data tools, dashboards, and analytics (internal & partners)
• Stack: Python · Dagster · R/Shiny · SQL · SAS · Power BI · SAP Analytics Cloud

Mar 2023 – Aug 2023

• Built an R/Shiny application to automate backtesting of credit stress test models — cutting report production from several days to 7 minutes
• Enriched performance indicators: PSI, HHI, AR/AUC, Hosmer-Lemeshow, MAE/RMSE, conformity tests
• Extended data quality diagnostics: missing values, outlier detection (Grubbs), population stability
• Conducted IFRS9 PD model backtesting: PiT PD vs ODR comparison, migration matrices, conservatism evaluation
• Analyzed climate stress tests (NGFS scenarios, transition & physical risks)
• Stack: Python · R/Shiny

• Feature engineering, Logistic Regression, Decision Tree, Random Forest, Bagging, XGBoost
• Class imbalance handling via SMOTE resampling; model evaluation & scorecard construction

• Segmentation: feature engineering, logistic regression, scorecard construction
• Calibration: Long-Run Average (LRA) estimation and Margin of Conservatism (MoC) quantification

🔗 github.com/tharoun/African-Countries-GNI-Per-Capita-Animation

• Animated bar chart race showing the evolution of the top 10 African countries by GNI per capita (1960–2024)
• Features country flags, World Bank income classification thresholds, and smooth year-by-year transitions
• Outputs available as MP4 video, HTML animation, or GIF
• Data sourced from World Bank World Development Indicators (Atlas method)
• Stack: Python · Matplotlib · Pandas · Pillow · FFmpeg

🔗 github.com/tharoun/wdi-data-pipeline

• Automated pipeline to download the full World Bank WDI dataset via API and load it into a PostgreSQL database
• Creates 6 structured tables: data, country, series, footnotes, and relationship tables
• Handles data cleaning, column standardization, schema management, and connection lifecycle
• Stack: R · WDI · RPostgres · DBI · tidyverse · PostgreSQL

🔗 github.com/tharoun/covid-2019-dashboard · 🌐 Live Demo

• Interactive dashboard to monitor the evolution of COVID-19 in Burkina Faso
• Built a data collection and processing pipeline to generate key epidemic indicators
• Features choropleth mapping and trend visualizations of case evolution
• Stack: R · Shiny · ShinyApps.io

Domain expertise: Basel Regulation · IFRS9 · Credit Risk (PD / LGD / CCF / EAD / RWA) · Backtesting · Stress Testing · Machine Learning · Spatial Econometrics · Data Warehousing

• 🏅 Designing Stories with SAP Analytics Cloud — 2025
• 🏅 Advanced Python Programming — Udemy, 2024
• 🏅 Data Analyst in Power BI — DataCamp, 2024
• 🏅 Quantitative Analyst Pro (Scoring, PD, LGD, Prudential Regulation) — RiskMDP Advisory
• 🏅 Base Programming SAS — 2022
• 🏅 Core Designer — Dataiku, 2022

• 🇬🇧 English — Fluent (speaking, writing, reading, listening)
• 🇫🇷 French — Native

Open to opportunities in Data Analytics, Data Science, and Quantitative Risk Modeling.
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