Senior Research Engineer at Google DeepMind
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Columbia University
Master’s Degree, Mathematics
January 1, 2016 – January 1, 2017
Ecole Nationale Supérieure des Mines de Nancy
Diplôme d'Ingénieur civil des Mines, Mathématiques Appliquées et Informatique
January 1, 2014 – January 1, 2017
Grenoble INP - UGA
Classes préparatoires aux Grandes Ecoles d'ingénieur, Mathematiques, Physique, Informatique, Chimie et Biologie
January 1, 2012 – January 1, 2014
Lycée Vaugelas
Baccalauréat (S-SVT Math), Mathématiques, Physique et Chimie
January 1, 2010 – January 1, 2012
Google DeepMind
Senior Research Engineer
April 1, 2026 – Present
New York, États-Unis
Senior Software Engineer, Machine Learning
September 1, 2022 – April 1, 2026
New York, États-Unis
Meta
Senior Software Engineer, Machine Learning
November 1, 2021 – September 1, 2022
New York, États-Unis
JPMorgan Chase & Co.
Executive Director, Lead Machine Learning Engineer
February 1, 2021 – November 1, 2021
JPMorgan Chase & Co.
Vice President, Machine Learning Engineer
February 1, 2019 – January 1, 2021
JPMorgan Chase & Co.
Associate, Machine Learning Engineer
August 1, 2017 – January 1, 2019
Spoken Language Understanding Using Long Short-Term Memory Neural Networks
September 1, 2016 – December 1, 2016
Replication of Microsoft research paper on the usage of Long-Short-Term-Memory Neural Network for Speech recognition: ■ Built from scratch in Theano (Research Deep Learning Python package), with bidirectional deep LSTM and attention mechanism as extra ■ Training NN on ATIS dataset (Airline Travel Information System) ■ Part of Deep Learning course at Columbia (Graded 87/100) Link to original paper: https://www.microsoft.com/en-us/research/publication/spoken-language-understanding-using-long-short-term-memory-neural-networks/
Systèmes de recommendations via l'Analyse de Concepts Formels (ACF)
October 1, 2015 – June 1, 2016
Découverte puis utilisation de l'Analyse de Concepts Formels pour bâtir en Python un système de recommendation de films par filtrage collaboratif basé sur l'algorithme APriori. Réalisé dans le cadre du projet de 2ème année. (Noté A) ■ Code disponible sous GitHub - Développement d'une librairie pour créer, manier et visualiser des concepts formels et des treillis de concetps ■ Implémentation informatique des méthodes pour déterminer les concepts d'un contexte formels ainsi que son treillis de concepts associé (lattices) : InClose et Breadth-First-Search ■ Incorporation algorithme APriori pour recommendation ■ Utilisation librairies Numpy, Pandas, Graphviz et structure de données Queue et Trie ■ Test sur une base de donnée réelle créé sur 50 films
Analyse des données de géolocalisation avec Google Maps Timeline
September 1, 2015 – February 1, 2016
En utilisant les données personnelles receuillies sur nos comptes Google, nous avons intégré, analysé et visualisé nos trajets quotidiens. Entièrement dévelopé en C. (Noté A) ■ Codage d'un parser JSON ■ Analyse des données pour calculer le temps moyen passé dans chaque lieu d'intérêt ■ Développement d'un algorithme pour déterminer le domicile et le lieu de travail ■ Réalisation d'une interface graphique avec la librairie GTK+ pour visualiser le calendrier
HSK 3
Confucius Institute Online
June 24, 2026 – Present
Statistics and R - Harvard
edX
June 24, 2026 – Present
Distributed ML with Apache Spark - Berkeley
edX
June 24, 2026 – Present
ML & NLP in Python
Udemy
June 24, 2026 – Present
Graph Analytics for Big Data - UCSD
Coursera Course Certificates
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (deeplearning.ai)
Coursera Course Certificates
June 24, 2026 – Present
Structuring Machine Learning Projects (deeplearning.ai)
Coursera Course Certificates
June 24, 2026 – Present
Natural Language Processing in Python
Udemy
June 24, 2026 – Present
Introduction to Apache Spark - Berkeley
edX
June 24, 2026 – Present
TOEFL (106/120)
ETS
June 24, 2026 – Present
Convolutional Neural Networks (deeplearning.ai)
Coursera Course Certificates
June 24, 2026 – Present
Big Data Analysis with Apache Spark - Berkeley
edX
June 24, 2026 – Present
Bayesian Statistics - Duke
Coursera Course Certificates
June 24, 2026 – Present
Machine Learning - Stanford
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's experience is heavily skewed towards Machine Learning Engineering and Research roles, primarily in large tech companies and financial institutions. While this demonstrates strong technical capabilities, the target role of 'Data Analyst' might not fully align with the candidate's senior-level ML/Deep Learning expertise. The projects show a strong inclination towards building complex algorithms and systems, which is more aligned with data science or ML engineering than traditional data analysis. The breadth of skills is strong in ML/DS, but less explicit in core data analysis tools beyond what's implied by ML work (e.g., specific BI tools, advanced SQL for reporting, data warehousing concepts).
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a structured approach to complex technical challenges. The academic and professional background suggests a high degree of self-motivation and ability to work in demanding environments. However, without psychometric test results or direct interview data, specific soft skills like teamwork, leadership, or stress handling cannot be definitively assessed.