CV

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• Professional Experience
• Programming Skills

Professional Experience

👨‍🚀 PhD in Economics - Collège de France

Dates: 2018-2021

Institution: Collège de France was founded in 1530, it is considered to be France’s most prestigious research establishment. 21 Nobel Prize winners and 9 fields medalists have been Full Professors at Collège de France.

What I did: My PhD in economics leverages Data Science and Natural Language Processing to track technology and knowledge diffusion across time and space. To do so, I worked with and created large patent and scientific papers datasets. Far from the image of a solitary work, I made it a team sport. I managed several Research Assistants, collaborated with and learnt from co-authors and developed an open source projects which found its fair amount of users in various communities. Last but not least, I had the opportunity to present my work at top tier international conferences.

More: I had the privilege to get guidance from Ph. Aghion (G-Scholar), my PhD advisor and one of the world-leading economist. I also had the chance to engage in active exchanges with top researchers (MIT, Stanford, Berkeley inter alia) and practitioners (Google patents, MIT Media Lab, WikiCite).

Key learnings: Project management, Cross disciplinarity, Diffusion of best technical and non-technical practices to junior fellows, Open source project development and dissemination.

👨‍🔬 Data Scientist

IPwe - Freelance

Dates: 2019-present

Company: IPwe is a startup on a mission to make the Intellectual Property market more efficient and transparent using Blockchain and Artificial Intelligence.

What I did: The overarching goal of my work at IPwe was to improve the patent information retrieval and analytics tools used by both the internal team and the clients. This involved a large amount of natural language processing and graph theory. I had the chance to get guidance from one of the co-founders, G. Karypis (G-Scholar), a Professor at the Department of Computer Science & Engineering at the University of Minnesota and Senior Principal Scientist at AWS. This was a game-changing experience and gave me a broader understanding of ML.

More: One thing that I like about early stage startups is that there is no clear distinction between research, engineering and operational implementation roles. This gave me the opportunity to broaden the range of my work: needs assessment, roadmap design, algorithm research and solution serving (API, model).

Key learnings:  Experimentation, On the job learning, Fast and good enough delivery.

Kayrros - Full time employee and Freelance

Dates: 2018-2019

Company: Kayrros is a fast growing start-up specialised in the production of unconventional data for the energy sector.

What I did: During my time at Kayrros I had the chance to work at the intersection between next generation energy data, economics, finance and data science. With Edgar G., my teammate and now friend, we first developed a reduced form model on the relation between oil storage and oil prices and tested econometrically the predicted causal relation between the two. Then, we used Kayrros in-house and near real-time in-tank oil storage in a “learning with experts” on-line oil price prediction algorithm. We carefully backtested the performance of our algorithm using historical data, notably finding sharpe ratio above 2 (high-performing trading strategy). The algorithm was then extended to hedging strategies.

More: Apart from our core contributions, we also had the chance to work closely with the oil storage product team as part of the release of an enriched version of the data.

Key learnings: Work under pressure, Deliver fast, Iterate faster, Sense of ownership, Working with friends makes everything easier.

🤓 Quantitative and Economic Researcher

French Development Agency - Consultant

Dates: 2017-2018

Company: The French Development Agency is a public financial institution fighting poverty and promoting sustainable development.

What I did: I was in charge of country risk assessment reports and participated to an on-site review in Serbia. This included meetings with the National Central Bank, the IMF local office, international banks as well as governmental agencies and non-governmental organisations.

More: Our work was directly used by the Country and Credit Risk Committee to decide on the country credit grade - determining the size and rate of the country credit facilities.

Key learnings: Work in a high stake environment, Commit to error-free and flawless work.

Natixis - Research Intern

Dates: 2015

Company: Natixis is a Corporate and Investment Bank.

What I did: In close collaboration with Sylvain Broyer (International Head of Economics at Natixis) and Emmeanuel S., a fellow intern and now friend, we designed research and empirical approaches to tackle key policy questions relevant to Natixis and its clients. This included macroeconomic and monetary policy questions. The outcome took the form of policy reports and research papers.

More: One of our research paper was later published in a peer-reviewed journal.

Key learnings: Team work, Self-driven.

TAC Economic and Financial

Dates: 2013-2014

Company: TAC Applied Economic and Financial Research is a consulting boutique located in Rennes (France) and New-Delhi (India). TAC works at the intersection of Economics, Finance and Machine Learning.

What I did: I was part of the development of a Political Risk Analytical tool. We used dimensionality reduction (PCA) and clustering to situate and cluster 190 countries on an institutional landscape. Each group was then related with a specific set of risks which could be monitored closely.

More: This is where I discovered the thrilling opportunities offered by Machine Learning. There was no way back!

Key learnings: Life is better with data and machine learning, Unsupervised learning is tricky, India is Incredible - indeed.

Programming Skills

✏️ Typescripts

🏷️ Git/Dev-Ops

🔮 MLOps

👨‍🔬 Data Science

☁️ Cloud

🤖 Deployment