Junior Professional Associate — Data Sciencist

The World Bank, Washington D.C., USA

Dec 2024 - Present

• Supported World Bank analytics on climate hazard exposure (floods, extreme heat) and urban resilience in South Asia.
• Built generative AI POC to optimize operational workflows and contributed to projects on urbanization, jobs, cooling, and health.

Data Consultant — Financial Services

Wavestone, Paris, France

Mar 2023 - Dec 2024

• Conducted market analysis for generative AI applications within financial institutions.
• Led a reverse-engineering Python project for COBOL code using LLMs.

Volunteer — Data Scientist

United Nations Development Programme (UNV - UNDP), Remote

Mar 2024 - October 2024

• Developed probabilistic Bayesian deep learning models using Python.
• Implemented multilabel text classification, enhancing accuracy and insight.

Data Scientist

Bloomin, Paris, France

May 2022 - Nov 2022

• Conducted statistical analysis using MCA and implemented a generalized ordinal model.
• Co-led a multilingual, multilabel text classification project using NLP and deep learning techniques.

Statistician, Economics Researcher

Institute for Research and Documentation in Health Economics (IRDES), Rennes, France

Oct 2021 - Mar 2022

• Conducted a comprehensive literature review on subjective health assessments.
• Analyzed health determinants with MIMIC and ordered probit models.

Data Scientist - Economic Affairs

United Nations Economic Commission for Africa (UNECA), Niamey, Niger (Remote)

Jul 2021 - Sep 2021

• Utilized e-commerce data to optimize intra-regional trade strategies within the AfCFTA.
• Developed a Python-based decision-support application for economic agents, implementing web scraping techniques for data collection, preparation and analysis.

Master’s degree in Engineering

National School for Statistics and Data Analysis (ENSAI), Rennes, France

Specialized in Data Science and Economics.
ENSAI is one of France’s most prestigious Grandes écoles d’ingénieurs specialized in Data Science. It’s courses content include:

• Machine Learning:
  supervised learning (e.g., regression, decision trees, neural networks, random forest, etc.);
  unsupervised learning (e.g., k-means, hierarchical clustering, KNN, NLP, etc.);
  deep learning (e.g., CNNs, LSTMs, RNNs, GANs etc.).
  
• Statistics and Mathematics:
  descriptive statistics (mean, outliers, etc.) and distributions (normal, Poisson, etc.);
  algebra and calculus (matrices, limits, optimization etc.);
  probability and hypothesis testing (significance level, confidence interval, p-value, t-test, etc.).
  
• Computer Science:
  database computation (with SQL and MySQL);
  data visualization and analytics (with matplotlib, ggplot2, dashboards, etc.);
  programming languages: Python, R, Stata and SQL.
  

Double Bachelor’s degree in Applied Economics

Paris Dauphine University - PSL, Paris, France

• Microeconomics and Macroeconomics
• international Economics
• Econometrics and Portfolia Management

Specializing in mathematics and physics

Pothier - Preparatory Classes for Grandes Ecoles (CPGE), Orléans, France

Maths, physics and engineering science studies preparing for the French Grandes Ecoles admission exams (CPGE).

• Mathematics: The mathematics level in CPGE is highly demanding, covering advanced areas such as analysis, algebra, and probability.
• Physics: In physics, I tackled in-depth subjects like mechanics, electromagnetism, and thermodynamics, with a rigorous theoretical and experimental approach.

Ranking: Ranked 1st out of 41 students, demonstrating proficiency in the subjects taught in CPGE.