About Me

I’m a Senior Data Scientist specializing in marketing analytics and machine learning operations with a PhD from École Polytechnique. I design, build, and deploy end-to-end ML solutions—from proof-of-concept to production—with a strong focus on experimentation and causal inference.

Currently at Air France-KLM, I work on personalizing customer experiences and optimizing digital marketing through rigorous A/B testing and recommendation systems. Previously, I increased conversion rates by 30% across 3 million customers at Senzai and published research at ICML 2023.

Core Expertise

My work focuses on developing innovative approaches that merge causality with machine learning for optimization and decision-making. Key areas include:

• Marketing Analytics: Customer segmentation, propensity modeling, campaign optimization
• Causal Inference: Treatment effect estimation, uplift modeling, A/B testing frameworks
• Statistical Learning: Bayesian modeling, uncertainty quantification, time series
• MLOps: Production pipelines, Docker, Kubernetes, Airflow, cloud infrastructure

Professional Experience

Air France-KLM | Senior Data Scientist - Marketing Operations Research (Nov 2025 - Present)

• Contributing to ‘Offer Display Recommender’ project to personalize customer product offerings
• Designing and executing A/B tests to optimize customer journey and flight selection experience
• Analyzing customer behavior and translating insights into strategies for business stakeholders

Mercor | Mathematics/Statistics Expert (Aug 2025 - Jul 2026)

• Writing mathematical problems to challenge reasoning capabilities of state-of-the-art language models
• Contributing to AI evaluation frameworks for leading research labs

Senzai | Senior Data Scientist & ML Engineer (Oct 2023 - Oct 2025)

• Designed recommendation system increasing conversion rates by 30% across 3M customers and 50M interactions
• Deployed causal inference ML models into production using Docker, Kubernetes, Airflow, MLflow on AWS
• Established MLOps practices and scalable backend pipelines (GitLab CI/CD)

namR | Data Scientist (May 2023 - Oct 2023)

• Developed features on large-scale datasets (50M+ rows on GCP)
• Optimized solar panel detection algorithm, reducing runtime from 25 to 8 hours (68% improvement)

TotalEnergies One Tech | Research Engineer - AI & Data Science R&D (Oct 2019 - Dec 2022)

• Completed Industrial PhD in Statistics & Machine Learning (École Polytechnique)
• Published research on Causal Meta-Learners at ICML 2023 (top-tier ML conference)
• Developed Gaussian Process models with 80% confidence for production prediction
• Patent filing for novel prediction methodology

Technical Stack

Programming & Machine Learning

• Languages: Python, R, SQL, MATLAB
• ML Libraries: Scikit-learn, XGBoost, LightGBM, CausalML, Shapely
• Data Processing: Pandas, NumPy, PySpark, SQLAlchemy
• Visualization: Matplotlib, ggplot2

MLOps & Infrastructure

• MLOps: Docker, Airflow, MLflow, DVC, GitLab CI/CD, Streamlit, FastAPI
• Cloud Platforms: AWS (EC2, Redshift, S3), GCP (BigQuery, Vertex.ai), Azure
• Databases: PostgreSQL, MySQL, Trino
• Tools: VSCode, PyCharm, Jupyter Notebook, Jira, Dataiku DSS, Grafana

Featured Publications

Title	Conference	Year	Link
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects	ICML paper	2023
Statistical Learning and Causal Inference for Energy Production	PhD Thesis	2022
Robust prediction interval estimation for Gaussian processes by cross-validation method	JSDA Journal	2022

Awards & Recognition

Total-IMT Data Challenge Winner (2018)

• Won Kaggle-style competition using Random Forest algorithms
• Awarded research internship with TotalEnergies

Scholarships and Grants

• Study Abroad Grant (3,100€) - Moroccan Ministry of Higher Education (2020-2022)
• Government Merit Grant (15,500€) - Moroccan Ministry of Higher Education (2016-2019)

Selected Talks & Conferences

International Conferences

• ICML 2023 - Honolulu, Hawaii, USA
  • Presented “Meta-Learners for Multi-Valued Heterogeneous Effects”
• SIAM UQ22 - Atlanta, USA
  • Presented “Robust Prediction Interval Estimation for Gaussian Processes”

Invited Talks

• University of Bern, Bern, Switzerland
  • Seminar on Gaussian Processes for Optimization
• Ionian University, Corfu, Greece
  • Greek Stochastics μ’: Causal Learning
• INRIA Saclay & LISN, Gif-Sur-Yvette, France
  • The Causal TAU seminar
• National Taiwan University, Remote
  • Machine Learning Summer School MLSS 2021

Open for Opportunities

I’m available for freelance projects and consulting engagements in marketing analytics, causal ML, and production systems. Interested in both project-based work and full-time opportunities with marketing technology companies.

Looking to collaborate? View my portfolio or get in touch.