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.