Portfolio
I Turn Data Into Revenue-Driving Marketing Insights
Specialized in causal inference, A/B testing, and recommendation systems that deliver measurable business impact—from €1M revenue lifts to 30% conversion increases.
Let's Work TogetherFeatured Case Studies
Driving €1M Monthly Revenue with Smart Flight Recommendations
The Challenge
When millions of travelers visit Air France-KLM websites to book flights, they face dozens of options. Which flight should rank first? Which fare class should we recommend? With millions of monthly visitors and bookings averaging €300-500, even a 0.5% improvement translates to significant revenue impact.
My Approach
I lead experimentation and evaluation for two critical recommender systems:
- Flight List Ranker: Personalizes flight rankings based on customer search behavior, booking history, and hundreds of signals to show flights customers are most likely to book
- Branded Fare Recommender: Predicts customer willingness to pay for premium features and personalizes fare class recommendations (Basic vs. Flex)
My role spans the complete lifecycle: offline evaluation on historical data → A/B test design and power analysis → live experiment monitoring → statistical analysis → final shipping decision.
Why It Matters
This isn't "we built a model and hope it works." Every recommendation goes through rigorous A/B testing on millions of real users. I own the entire process—from designing experiments to making final deployment decisions. At this scale, even small optimizations drive massive business value.
30% Conversion Lift Through Marketing Campaign Optimization
The Challenge
Digital marketing campaigns were struggling with low conversion rates and inefficient customer targeting. The startup needed a scalable ML solution to optimize campaign performance across millions of customers and interactions.
My Approach
Designed and deployed an end-to-end recommendation system for customer targeting optimization:
- Causal ML Models: Implemented uplift modeling and treatment effect estimation to identify high-propensity customers
- Production Infrastructure: Built scalable MLOps pipelines using Docker, Kubernetes, Airflow, and MLflow on AWS
- Data Engineering: Processed 50M+ interaction events across 3M customer profiles
Key Achievement
Established MLOps best practices and production-ready infrastructure that reduced time-to-deployment for new models from weeks to days. This wasn't just a model—it was a complete, scalable system.
AI Evaluation for State-of-the-Art Language Models
The Challenge
As LLMs become increasingly capable, robust evaluation methodologies are needed to test true reasoning vs. pattern matching. Leading AI labs needed challenging mathematical and statistical problems to benchmark frontier models.
My Approach
Designed advanced mathematical problems spanning probability theory, statistical inference, optimization, and causal reasoning. Problems require multi-step reasoning and proper interpretation of uncertainty—areas where LLMs often struggle.
Impact
Contributed to evaluation frameworks used by leading AI research labs, helping advance understanding of LLM mathematical capabilities and reasoning limitations.
My Process
How I Drive Measurable Impact
A systematic approach from data to deployment, ensuring every insight translates to business value.
Understand the Problem
Align with stakeholders on business objectives, success metrics, and constraints. Define clear KPIs.
Data & Analysis
Explore customer behavior patterns, build features, and validate data quality. Perform exploratory causal analysis.
Design Experiments
A/B test design, power analysis, sample size calculations. Offline evaluation to validate approach before going live.
Build & Deploy
Develop ML models, establish MLOps pipelines, deploy to production with monitoring and alerting.
Monitor & Iterate
Real-time experiment monitoring, statistical analysis, and rapid iteration based on results.
Deliver Impact
Present findings, make data-driven shipping decisions, measure business outcomes, and scale successes.
Clients & Testimonials
— Marketing Operations Lead, Major European Airline
Select Clients & Projects:
- Air France-KLM: Recommendation systems & A/B testing for flight booking optimization
- Senzai: End-to-end ML infrastructure for marketing campaign optimization
- Mercor: AI evaluation frameworks for frontier language models
- TotalEnergies: Causal inference research (ICML 2023 publication)
Core Competencies
Marketing Data Science
- Customer segmentation & propensity modeling
- Campaign optimization & attribution
- Personalization engines
- Conversion rate optimization
Experimentation & Causal Inference
- A/B testing design & power analysis
- Treatment effect estimation
- Uplift modeling & meta-learners
- Sequential analysis
Recommendation Systems
- Ranking algorithms (learning-to-rank)
- Collaborative filtering
- Contextual bandits
- Offline/online evaluation
Technical Stack
- Python, R, SQL
- AWS, GCP (BigQuery), Azure
- Docker, Kubernetes, Airflow, MLflow
- Scikit-learn, XGBoost, CausalML
About Me
PhD in Statistics & Machine Learning from École Polytechnique with 7+ years building production ML systems. Published at ICML 2023. Based in Paris, working with clients across Europe and North America. Fluent in English, French, and Arabic.
Core strengths: Combining academic rigor with business pragmatism. I don’t just build models—I drive measurable revenue impact through rigorous experimentation and causal inference.
Let’s Work Together
I’m available for freelance projects and full-time opportunities in:
- Marketing analytics & customer intelligence
- A/B testing & experimentation programs
- Recommendation systems & personalization
- Causal ML & treatment effect estimation
- MLOps & production ML systems
Ready to drive measurable impact? Get in touch