Senior Staff Software Engineer - Google DeepMind
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Columbia University
Master of Science (M.Sc.), Operations Research
January 1, 2014 – January 1, 2015
École Polytechnique
Master of Science (M.Sc.), Applied Mathematics
January 1, 2011 – January 1, 2015
Google DeepMind
Senior Staff Software Engineer
May 1, 2024 – Present
Staff Software Engineer - Google Research
November 1, 2022 – May 1, 2024
Senior Software Engineer - Google Research
November 1, 2020 – October 1, 2022
Sofware Engineer - Google Brain
April 1, 2019 – October 1, 2020
Machine Learning Deployment Engineer - Google Cloud
April 1, 2017 – April 1, 2019
KPMG US
Data Scientist
June 1, 2015 – April 1, 2017
New York City Metropolitan Area
AXA en France
Financial Risk Analyst
April 1, 2014 – July 1, 2014
Conergy AG
Global Purchasing Department
July 1, 2013 – August 1, 2013
Hamburg, Germany
Brigade de sapeurs-pompiers de Paris (BSPP)
Leadership Internship
September 1, 2011 – April 1, 2012
Paris
Sentiment Analysis on Tweets
April 1, 2015 – Present
Github: https://github.com/flavienprost/Tweet-analysis Sentiment analysis consists in identifying sentiments in sentences. Here our target is to classify tweets between negative and positive opinions. This project introduces a new classifier for sentiment analysis on tweets. The complexity of tweets compared to other corpus relies in its short length and its spelling errors. The tweet dataset comes from the Sentiment140 corpus and is composed of 1.6 million tweets automatically annotated using emoticons. Different embedding are used and compared in this project. We used both bag of word representation associated to feature selection and Doc2Vec which is based on Google recent research. Furthermore we came up with a new polarity grained based representation. We built various classifiers on these embeddings and compared their results. Eventually we combined them through ensemble learning methods. We reached a 80.1% accuracy.
Statistical Simulation of evolution of populations
March 1, 2014 – Present
- Modeling of a sexual reproduction of a population based on Galton-Watson asexual model - Emphasis on some extinction criteria based on the parameters of the distributions - Simulations with different matching functions in Matlab
Cultural Fit Analysis
The candidate's diverse project experience, from academic statistical simulations to real-world sentiment analysis, and their progression through various roles at Google (including research and deployment) suggest a strong drive for learning and contribution. Their background in operations research and applied mathematics, combined with practical ML experience, indicates a strong analytical mindset. The target role of 'Data Analyst' is a slight mismatch with their recent senior-level ML/AI engineering roles, which are typically more advanced than a standard Data Analyst position. However, their foundational data science experience at KPMG aligns better with the target role.
Soft Skills & Operational Fit
The candidate's experience leading a team during a leadership internship with the fire brigade suggests strong leadership and teamwork skills. Their progression through various roles at Google, including research and deployment, indicates adaptability and problem-solving capabilities. However, specific details on communication style or stress handling are not available from the provided data.