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cv.tex
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% "ModernCV" CV and Cover Letter
% LaTeX Template
% Version 1.1 (9/12/12)
%
% This template has been downloaded from:
% http://www.LaTeXTemplates.com
%
% Original author:
% Xavier Danaux (xdanaux@gmail.com)
%
% License:
% CC BY-NC-SA 3.0 (http://creativecommons.org/licenses/by-nc-sa/3.0/)
%
% Important note:
% This template requires the moderncv.cls and .sty files to be in the same
% directory as this .tex file. These files provide the resume style and themes
% used for structuring the document.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%----------------------------------------------------------------------------------------
% PACKAGES AND OTHER DOCUMENT CONFIGURATIONS
%----------------------------------------------------------------------------------------
\documentclass[12pt,a4paper,sans]{moderncv}
\moderncvstyle{classic} % CV theme - options include: 'casual' (default), 'classic', 'oldstyle' and 'banking'
\moderncvcolor{blue} % CV color - options include: 'blue' (default), 'orange', 'green', 'red', 'purple', 'grey' and 'black'
\usepackage[scale=0.90]{geometry}
%----------------------------------------------------------------------------------------
% NAME AND CONTACT INFORMATION SECTION
%----------------------------------------------------------------------------------------
\firstname{\textcolor{color1}{Thomas}}
\familyname{\textcolor{color1}{Chauvet}}
\address{Geneva, Switzerland}{}{}
\homepage{thomas.chauvet@protonmail.com}
\social[linkedin]{www.linkedin.com/in/thomaschauvet/}{}
\social[github]{www.github.com/thomas-chauvet/}
%----------------------------------------------------------------------------------------
\begin{document}
\makecvtitle % Print the CV title
\vspace{-1.0cm}
\begin{center}
\Large{\textbf{\textcolor{color1}{Lead Data Engineer / Scientist}}} - 9 years of experience
\end{center}
\textcolor{color2}{Data enthusiast, my aim is to build data products to have a positive impact.}
\vspace{0.2cm}
%----------------------------------------------------------------------------------------
% WORK EXPERIENCE SECTION
%----------------------------------------------------------------------------------------
\section{Experiences}
\cventry{June 2022 \\--\\ December 2023}{Lead Data Engineer}{\textsc{E-nno} Energy efficiency startup}{Geneva, Switzerland}{}{
\begin{itemize}
\item \textbf{Design, build and maintain data stack:}
\begin{itemize}
\item \textbf{Streaming engine} to collect and process data (telemetry from IoT devices) with \textit{Kafka}, \textit{Kafka-Connect} and \textit{Faust} (Python streaming engine),
\item Batch data pipelines orchestrated with Airflow on Kubernetes,
\end{itemize}
\item Collaborate on analytics and dashboard tools.
\end{itemize}
Technology stack: Python, Kafka, PostgreSQL, Airflow, Kubernetes, Github-actions
}
\vspace{0.3cm}
\cventry{September 2021 \\--\\ May 2022}{Lead Machine Learning Engineer}{\textsc{Rocket software}}{Geneva, Switzerland}{}{
\begin{itemize}
\item \textbf{Design and build a machine learning project for time series forecasting}
\item Analyze and understand business needs,
\item Explore and iterate on different machine learning models,
\item Build the full data pipeline to extract, transform and load data needed for the ML model. Package everything for on-premise deployment.
\end{itemize}
Technology stack: Python, ElasticSearch, MLflow, Docker, Jenkins
}
\vspace{0.3cm}
\cventry{May 2019 \\--\\ July 2021}{Artificial Intelligence Engineer}{\textsc{International Committee of the Red Cross (ICRC)}}{Geneva, Switzerland}{}{
\textit{Artificial Intelligence Engineer at ILEM Group in contract with ICRC.}
\textit{Most of the project is confidential. Two engineers, including myself, in the tech-team.}
\begin{itemize}
\item Development of a \textbf{search engine on distributed and segregated databases} with textual and image fields from scratch.
Focus on search, data pipelines, streaming engine, back-end and industrialization of the product.
\item Strong collaboration with the business to understand the need and translate it in technical architecture/implementation.
\end{itemize}
Technology stack: Python (\textit{Faust, FastAPI}), Elasticsearch, Kafka, Docker, Ansible.
}
\vspace{0.3cm}
\cventry{Aug. 2017 \\--\\ March 2019}{Data Scientist / Engineer}{\textsc{Bleckwen} Fintech startup}{Paris, France}{}{
\begin{itemize}
\item R\&D on \textbf{temporal graph and text mining} to fight fraud. Use of graph mining to extract patterns and find anomalies in transactions network.
\item Development of an anti-fraud product for banks from scratch. \textbf{Streaming machine learning} engine to score transactions with high latency constraints.
Main responsibility on engine module developed with \textit{Flink} to collect, process and score transactions.
Add (hot) machine learning model deployment and machine learning interpretability in the streaming engine.
\end{itemize}
Technology stack: Python (\textit{Pandas, scikit-learn, Keras}), Scala, Kafka, Flink, Docker.
}
\vspace{0.3cm}
\cventry{Feb. 2015\\--\\Jul. 2017}{Data Scientist}{\textsc{Deepki} Energy Efficiency startup}{Paris, France}{}{
\begin{itemize}
\item Data collection, cleaning and consolidation from different sources (invoices, telemetry, patrimonial data, activity).
Use of Machine learning to understand buildings consumption thanks to clustering.
Use of supervised learning to \textbf{predict consumption}, to \textbf{detect anomalies}, and to simulate renovation impact.
\item First employee to work on the product creation. Software as a Service (SaaS) to visualize, understand, find patterns in energy data and manage recommendations and alerts.
\item Supervision of two engineering school student projects and interns.
\end{itemize}
Technology stack: Python (\textit{scikit-learn, Pandas, Flask}), R (\textit{tidyverse: dplyr, tidyr, ggplot2, Shiny}).
}
%\cventry{Jul. -- Sept. 2014}{Data Scientist Intern}{\textsc{French National %Public Health Agency - formerly InVS} Montpellier, France}{}{}{The aim of %the internship was the early detection of aberration like epidemic %outbreak, bacteriological attack, etc. on time series data.
%\begin{itemize}
%\item Analyze and implement methods to detect unusual events based on %CUSUM (CUmulative SUM) and its variation on real time series data in %\textit{R}. Selection of the most relevant methods and combination to %increase the reliability of the warning system,
%\item Development of an interactive tool in \textit{Shiny} to visualize %epidemiological time series and manage alerts (detection of epidemic %peaks). Users were epidemiologists.
%\end{itemize}
%}
%----------------------------------------------------------------------------------------
% COMPUTER SKILLS SECTION
%----------------------------------------------------------------------------------------
\section{Computer skills}
\cvitem{Languages}{Python, R, Scala}
\cvitem{Storage}{PostgreSQL, TimescaleDB, Elasticsearch, Kafka}
\cvitem{Processing}{DuckDB, Kafka ecosystem, Faust (python streaming engine), Flink}
\cvitem{Orchestrator}{Airflow}
\cvitem{Dashboard}{Kibana, Grafana}
\cvitem{Tools}{Git, Docker, Kubernetes, Github-action}
\cvitem{Others}{Scikit-learn, FastAPI, Typer, Pydantic, Dynaconf, MLflow, Pandas, Polars}
%----------------------------------------------------------------------------------------
% EDUCATION SECTION
%----------------------------------------------------------------------------------------
\section{Education}
\cventry{2012 -- 2015}{INSA}{Toulouse, France}{French Graduate Engineering School}{}{Engineering degree (M.Sc.) in computational and mathematical engineering, specialization in statistical methods and models option Data Science.} % Arguments not required can be left empty
\cventry{2009 -- 2012}{INSA}{Lyon, France}{}{}{Intensive undergraduate-level preparation in advanced Science (Mathematics, Physics, Mechanics, Chemistry).}
%----------------------------------------------------------------------------------------
% LANGUAGES SECTION
%----------------------------------------------------------------------------------------
\section{Languages}
\cvitem{}{
Native \textbf{French} speaker.
Fluent in \textbf{English} (TOEIC: 900).
}
\end{document}