Senior Machine Learning Engineer @ Spendesk
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University of Lille 1 Sciences and Technology
Master of Science in Statistics and Numerical Engineering (M2 Ingéniérie Statistique et Numérique)
January 1, 2015 – January 1, 2016
Université Côte d'Azur
Master of Science in Applied Mathematics (Master recherche), Mechanical Engineering
January 1, 2012 – January 1, 2013
Polytech Nice Sophia
Master of Science in Engineering (Diplôme d'ingénieur), Applied Mathematics and Modeling
January 1, 2010 – January 1, 2013
Spendesk
Senior Machine Learning Engineer
March 1, 2024 – Present
Spendesk
Machine Learning Engineer
September 1, 2022 – February 1, 2024
Allianz Trade
Machine Learning Engineer
March 1, 2020 – August 1, 2022
Tinyclues
Machine Learning Engineer
November 1, 2016 – February 1, 2020
Greater Paris Metropolitan Region
Data Science Retreat
Participant
September 1, 2016 – November 1, 2016
Berlin Metropolitan Area
Criteo
R&D intern
March 1, 2016 – August 1, 2016
Greater Paris Metropolitan Region
Accenture
Analyst
February 1, 2014 – August 1, 2015
University of Michigan
Visiting Scholar
March 1, 2013 – September 1, 2013
Ann Arbor, Michigan
Diebold Nixdorf
Engineering Intern
June 1, 2012 – September 1, 2012
Université de Nantes
Research Intern
July 1, 2010 – August 1, 2010
Nantes
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
DeepLearning.AI
June 24, 2026 – Present
Mining Massive Datasets
Stanford University
June 24, 2026 – Present
Simulation and modeling of natural processes
University of Geneva
June 24, 2026 – Present
Deep Learning Specialization
DeepLearning.AI
June 24, 2026 – Present
Sequence Models
DeepLearning.AI
June 24, 2026 – Present
Convolutional Neural Networks
DeepLearning.AI
June 24, 2026 – Present
Recommender Systems: Evaluation and Metrics
University of Minnesota
June 24, 2026 – Present
Introduction to Recommender Systems: Non-Personalized and Content-Based
University of Minnesota
June 24, 2026 – Present
Matrix Factorization and Advanced Techniques
University of Minnesota
June 24, 2026 – Present
Inferential Statistics
Duke University
June 24, 2026 – Present
Generative AI with Large Language Models
DeepLearning.AI
June 24, 2026 – Present
Natural Language Processing with Classification and Vector Spaces
DeepLearning.AI
June 24, 2026 – Present
Structuring Machine Learning Projects
DeepLearning.AI
June 24, 2026 – Present
Neural Networks and Deep Learning
DeepLearning.AI
June 24, 2026 – Present
Nearest Neighbor Collaborative Filtering
University of Minnesota
June 24, 2026 – Present
Machine Learning
Stanford University
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career progression through various companies (Spendesk, Allianz Trade, Tinyclues, Criteo, Accenture) and participation in a Data Science Retreat indicates adaptability and a drive for continuous learning. The breadth of certifications in diverse ML areas (NLP, Recommender Systems, Deep Learning) suggests intellectual curiosity. However, the target role of 'Data Analyst' is a shift from their primary 'Machine Learning Engineer' experience. While ML engineers possess strong analytical skills, the core responsibilities and day-to-day tasks of a Data Analyst often involve more direct business stakeholder interaction, dashboarding, reporting, and less emphasis on model deployment and infrastructure. The cultural fit for a pure Data Analyst role might require further validation of their interest and experience in data visualization, business intelligence tools, and translating complex data into actionable business insights, which are not explicitly detailed in the provided experience descriptions. The current experience is heavily skewed towards ML engineering, which might not be a direct fit for a traditional Data Analyst role without further clarification.
Soft Skills & Operational Fit
The candidate's resume highlights a strong technical background and a consistent career path in Machine Learning Engineering. While the descriptions are concise, they indicate a focus on industrialization, monitoring, and optimization, suggesting an operational mindset. The numerous certifications demonstrate a proactive approach to skill development. However, without specific project details or direct communication samples, it's difficult to assess soft skills like collaboration, problem-solving communication, or leadership style. The transition from ML Engineer to Data Analyst would require demonstrating strong analytical storytelling and business insight generation, which are not explicitly detailed in the current experience descriptions.