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Software Engineer at Google
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, Operations Research
January 1, 2014 – January 1, 2015
École Polytechnique
Master's Degree, Applied Mathematics
January 1, 2011 – January 1, 2015
Software Engineer
January 1, 2018 – Present
Greater New York City Area
McKinsey & Company
Data Scientist
July 1, 2017 – January 1, 2018
McKinsey & Company
Data Scientist - junior
August 1, 2015 – June 1, 2017
Columbia University in the City of New York
Course Assistant - Algorithmic Trading
January 1, 2015 – May 1, 2015
Schlumberger
Engineering - R&D
April 1, 2014 – July 1, 2014
Tokyo, Japon
Thales
Operations Analyst
July 1, 2013 – August 1, 2013
Singapour
French Army
Officer Cadet
September 1, 2011 – April 1, 2012
Beatles songs labelling
February 1, 2015 – Present
Labelled sequences of chords for a set of songs from the Beatles. Used Structured-SVM to deal with the very high number of outcomes and Viterbi algorithm to compute the most likely sequence of chords for each song. Python.
Kaggle - Axa Driver Telematics Analysis
January 1, 2015 – March 1, 2015
Kaggle competition with Axa. Used telematic data to identify a driver signature. Developed algorithms to identify drivers from their driving characteristics. Python. Ranked 83rd / 1528
Slovix
June 1, 2013 – Present
Website allowing users to trade easily small amounts of money to each other. Network-based: trades between friends.
Desktop-search
May 1, 2013 – Present
Desktop-search system in Java. Designed to perform fast searches across a big mass of data. Create an index taking into account all the files and folders of a specific directory. Allows the user to search sentences in the files. The program return the most relevant files based on word frequency.
Research project - Log different
September 1, 2012 – May 1, 2013
Group project with Java and GIT. Developed a software to identify users from the signature they draw on a laptop trackpad. Designed recognition methods based on signature motion dynamics.
Cultural Fit Analysis
The candidate's background includes roles at large, reputable companies (Google, McKinsey) and academic institutions, suggesting an ability to thrive in structured and high-performance environments. The personal projects, especially the Kaggle competition and research projects, indicate a strong drive for continuous learning and independent problem-solving, which aligns well with innovative cultures. The diverse experiences, from software engineering to data science and even military service, suggest a broad perspective and adaptability. However, the target role is 'Data Analyst', and while the candidate has strong data science experience, the direct alignment with a pure 'analyst' role needs further validation to ensure cultural fit with typical analyst responsibilities.
Soft Skills & Operational Fit
The candidate's project descriptions suggest a proactive and problem-solving attitude, particularly in data-intensive challenges. Experience as a Course Assistant and Officer Cadet indicates leadership potential and structured thinking. The diverse project portfolio suggests adaptability and a willingness to tackle complex problems. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.