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Curriculum Vitae

Education

École des Mines de Paris (Mines ParisTech) – Paris

Civil Engineer – Sept. 2020 – Mar. 2022

  • Specialization in Geostatistics and Applied Probability.

École supérieure de physique et chimie industrielles de Paris (ESPCI Paris) – Paris

Engineer – Physics Option – Sept. 2017 – Sept. 2020

  • Multidisciplinary scientific foundation: mathematics, physics, chemistry, and biology.
  • Major in Biophysics.

Lycée Hoche – Versailles

Preparatory Class for Grandes Écoles – Physics & Chemistry Option – Sept. 2015 – Aug. 2017

  • Multidisciplinary scientific education: mathematics, physics, and chemistry.
  • Preparation for entrance exams to top engineering schools.

Experience

Artelys

Consultant – Data Science – May 2023 – Present

  • Develop operational solutions (dashboards, applications) for data science models, including reporting libraries, data visualization, APIs, and ML modeling.
  • Apply data science for energy, focusing on time series prediction, clustering, and climate correction.
  • Involved in DataOps and DevOps, deploying applications and microservices (Database, API, Dashboard).
  • Conduct R training sessions, covering basic R to deployment of an electricity consumption data visualization dashboard.

RTE (Réseau de Transport d’Électricité)

Engineer – Market Modeling – Mar. 2022 – Mar. 2023

  • Modeled investments and behaviors of energy market players and capacity.
  • Developed an R model for investment modeling and simulation, enhancing the Antares Simulator (European electricity system simulator).

Intern – Network Modeling Engineer – Sept. 2021 – Mar. 2022

  • Conducted prospective studies on the economic benefits of batteries for managing grid congestion by 2050.
  • Modeled network investments related to stationary storage (batteries) for regional grids.
  • Developed and maintained a Python heuristic for calculating network investments and battery behavior.

International Energy Agency (IEA)

Intern – Modeling Engineer – Mar. 2021 – Sept. 2021

  • Developed a Python model to disaggregate electricity demand by subsector of use using deep learning (MLPs) for multiple countries.
  • Contributed to the 2021 World Energy Outlook in the demand-side section.

Languages & Skills

Programming

  • Python
  • R
  • SQL

Software & other tools

  • Docker
  • Airflow
  • Slurm
  • PostgreSQL

Languages

  • English (Fluent), Spanish (Intermediate)