Amine Biad

•Developed an incentive optimization model that reduced marketing spend while increasing GMV by €15M, leveraging sensitivity analysis to identify key drivers of user behavior.

•Applied SciPy optimization algorithms to design synthetic control models, quantifying the impact of opening new store locations and improving decision-making for expansion strategies.

•Designed and executed A/B testing frameworks to validate operational strategies, leading to a 9% increase in user retention and an additional €10M in GMV.

•Built Markov chain models to analyze user behavior transitions, enabling accurate predictions of long-term retention patterns and improving customer lifecycle management.

•Streamlined experimentation processes by creating a Python package for A/B testing and statistical analysis, reducing analysis time and improving reproducibility.

•Optimized logistic regression models, driving a 2% increase in bidding efficiency and reducing operational costs.

•Designed and executed hypothesis tests to validate model improvements, leading to a 1.5% increase in ad performance and precision.

•Developed data pipelines tailored for high-frequency user location data, improving processes for data ingestion, transformation, and storage, thus enabling faster data analysis.

•Developed Markov chain models to analyze user location transitions, improving fraud detection accuracy by 20% and operational efficiency.

•Created predictive models for user demographics and fraud detection, reducing operational costs.