Data Scientist and AI Engineer who turns complex data into decisions that matter.
Three internships, 15+ production projects: diagnosed a $32.4M retail margin crisis across 504K transactions, scored the financial health of 9,618 African SMEs, forecasted electricity demand for 5 Moroccan cities, and built an NLP compliance pipeline for EU Pay Transparency regulation.
What I build: end-to-end ML systems (XGBoost, LightGBM, PyTorch, SHAP, MLflow), agentic RAG pipelines with hallucination detection and evaluation gates (Ragas, DeepEval, Langfuse), and GenAI copilots that let decision-makers interrogate live analytics in natural language.
The hardest part is never the model. It's framing the right problem and communicating the finding to someone who needs to act on it. I've trained in McKinsey's framework for exactly that: hypothesis-led problem solving, MECE issue trees, and the Pyramid Principle.
Data engineering is my foundation: Airflow, dbt, Snowflake, Kafka, Spark. I build the pipelines that feed the models, not just the models.
🔵 Currently: End-of-Studies Internship @ GeTeam Building an AI system for equal-value job identification under EU Pay Transparency Directive 2023/970
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