I'm a quantitative Data Scientist & Analyst with a strong background in statistical modeling, data pipeline engineering, credit risk modeling and trade statistics. 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.

Over the past 3 years, I have worked as an International Data Consultant at the UN (ITC, Geneva) and as an Intern Statistician at the WTO, contributing to trade competitiveness analytics, data pipeline engineering, and econometric research. I also contributed to the WTO publication "Trade for Peace: Pathways to Sustainable Trade and Peace" (2025).

Jun 2024 - Feb 2026

• Automated data pipelines for trade data processing: quality control, cleaning, standardization and indicator production (Python, Dagster)
• Designed visualizations and quality monitoring dashboards for trade flow analysis (Power BI, R/Shiny, SAP Analytics Cloud)
• 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)
• Developed and used AI tools (LLM/Claude, SAP Analytics Cloud AI) for product classification and code generation
• Stack: Python · Dagster · R/Shiny · SQL · SQL Server · SAS · Power BI · SAP Analytics Cloud · Git · AWS

Sept 2023 - May 2024

• Contributed to Chapter 3 of Trade for Peace: Pathways to Sustainable Trade and Peace (WTO, 2025): integrated trade and peace datasets (IEP, Global Peace Index), applied econometric time-series models and correlation analyses on WTO membership and peace dynamics
• Contributed to the DG Annual Report on Accessions: drafted the trade policy and market-access analysis section for Article XII countries
• Participated in the final Working Party meetings for the accession of Comoros and Timor-Leste
• Collected, cleaned and analysed trade, conflict and peace datasets from multiple international sources (IEP, WTO, UN)
• Stack: Python · R · Stata · SQL

Mar 2023 - Aug 2023

• Built an R/Shiny application to automate backtesting of credit stress test models - cutting report production from several days to 15 minutes
• Conducted IFRS9 PD model backtesting: PiT PD vs ODR comparison, migration matrices, conservatism evaluation and recommendations
• Enriched performance indicators: PSI, HHI, AR/AUC, Hosmer-Lemeshow, MAE/RMSE, conformity tests
• Extended data quality diagnostics: missing values, outlier detection (Grubbs), population stability
• Analyzed climate stress tests under NGFS scenarios (transition & physical risks)
• Stack: Python · R/Shiny

Jun 2022 - Dec 2022 | Remote

• Advised government officials from 5 African countries on trade data quality standards, governance frameworks and information-sharing protocols during ATO workshops
• Prepared and delivered data quality reports for ATO partner countries, identifying inconsistencies and recommending corrective actions
• Enhanced firm-level and aggregate trade data used for regional competitiveness monitoring
• Upgraded the firm classification system (business type classification) to improve data categorisation across ATO datasets
• Contributed to the development of the African Trade Digital Interconnectivity (AfTDI) application
• Stack: R · SQL · Excel

Mar 2021 - Aug 2021 | Project: MARS (Modelisation, Apprentissage et Resilience des Socioecosystemes)

• Designed a stochastic multi-compartment epidemic model (SEIRDS) as a data simulator for training and evaluation
• Implemented and optimised Echo State Networks (Reservoir Computing) for epidemic time series prediction on simulated data
• Evaluated model performance and sensitivity: RMSE analysis, prediction horizon sensitivity, robustness testing
• Applied Deep Learning methods to a complex dynamical systems problem under supervision of CIRAD/IRD researchers
• Stack: Python · NumPy · Matplotlib

• Feature engineering, Logistic Regression, Decision Tree, Random Forest, Bagging, XGBoost
• Class imbalance handling via SMOTE resampling; model evaluation & scorecard construction
• Stack: Python · Scikit-learn · XGBoost

• Segmentation: feature engineering, logistic regression, scorecard construction
• Calibration: Long-Run Average (LRA) estimation and Margin of Conservatism (MoC) quantification in compliance with Basel/IFRS9 requirements
• Stack: Python · R

🔗 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 (Basel 3/4) · IFRS9 · Credit Risk (PD / LGD / CCF / EAD / RWA) · Backtesting · Stress Testing · Machine Learning · Spatial Econometrics · Trade Statistics · Data Warehousing · Data Pipeline Engineering

Low, P., Piermartini, R. & Miashiro, A. (Eds.) Trade for Peace: Pathways to Sustainable Trade and Peace -World Trade Organization, 2025

Contributed to Chapter 3: statistical analysis and data preparation, integration of trade and peace datasets (IEP, Global Peace Index), econometric time-series modeling and correlation analyses on WTO membership and peace dynamics. Collaboration with researchers from the Institute for Economics and Peace.

• 🏅 Designing Stories with SAP Analytics Cloud -SAP, 2025
• 🏅 Advanced Python Programming -Udemy, 2024
• 🏅 Data Analyst in Power BI -DataCamp, 2024
• 🏅 Statistics of International Trade in Services (SITS) -UNCTAD TrainForTrade, 2024
• 🏅 International Merchandise Trade Statistics (IMTS) -UNCTAD TrainForTrade, 2024
• 🏅 Reproducible Research Fundamentals -World Bank DIME Learning Series, 2023
• 🏅 Quantitative Analyst Pro (Scoring, PD, LGD, Prudential Regulation) -RiskMDP Advisory
• 🏅 Base Programming SAS -SAS Institute, 2022
• 🏅 Core Designer -Dataiku, 2022

• 🇬🇧 English -Full professional proficiency
• 🇫🇷 French -Native

Open to opportunities in Data Analytics, Data Science, and Quantitative Risk Modeling.
📬 [email protected] · LinkedIn · GitHub