Alexis Dubouloz-monnet
Freelance - Consultant Google Ads & Meta Ads at AAC
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolAlexis Dubouloz-monnet is a seasoned Google Ads and Meta Ads consultant with 11 years of commercial experience and nearly three years at Google Ads complemented by agency and freelance roles. Based in Paris, he partners with e-commerce and lead-generation clients across industries from fashion and cosmetics to legal and real estate, blending a scientific background and commercial training in agri-food business. Alexis emphasizes trust, transparency and long-term partnership over transactional client-provider dynamics, and he repeatedly optimizes campaigns for diverse budgets and verticals. Less obvious: he also contributes to open-source ML projects, adding offline RL methods and multi-agent environment improvements to prominent Google research repositories, showing a technical curiosity that informs data-driven ad strategies. Practical, analytic and client-focused, he helps brands translate experimentation and research-grade thinking into measurable media performance.
11 years of coding experience
1 year of employment as a software developer
Certificate - Introduction to AeronauticsDUT génie biologique option industries alimentaires et biologiques, DUT génie biologique option industries alimentaires et biologiques at Université de Pau et des Pays de l'AdourA-level, A-level at Jean Cassaigne High SchoolMaster of Arts Agri-Food Business Manager, Master of Arts Agri-Food Business Manager at ISEMA : Ecole supérieure de commerce
Github Skills (12)
machine-learning10
jax10
tdd10
python10
reinforcement-learning10
numpy9
gymnasium9
openai-gym9
agent8
algorithms8
testing8
ai8
Programming languages (4)
Github contributions (5)
google-deepmind/acme
Feb 2022 - Jun 2022
A library of reinforcement learning components and agents
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 2 comments in 3 months
Contributions summary:Alexis primarily contributes to reinforcement learning agents and algorithms within the Acme library. They added options for offline reinforcement learning methods like TD3+BC and CRR, which involved modifications to the TD3 learner class, including new hyperparameters and loss calculations. The user also integrated SARSA targets and provided example implementations for offline training, modifying dataset iterators. Moreover, they corrected the TD3+BC implementation.
reinforcement-learningreinforcementagentsdeep-reinforcement-learning
google-research/google-research
Jul 2019 - Apr 2020
Google Research
Role in this project:ML Engineer
ML EngineerContributions:6 commits in 8 months
Contributions summary:Alexis primarily contributed to the `psycholab` module, focusing on the development of multi-agent grid world games for reinforcement learning research. They implemented game mechanics and action spaces, as well as functions for state representation such as `discrete_state` and `one_hot_state`. Furthermore, they incorporated gym-like action spaces, expanding the library's integration with reinforcement learning environments. The user's changes also included updates to test files, demonstrating a focus on ensuring the proper functionality of the game environment.
googlemachine-learningai
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