Stagiaire Data Scientist/Data Engineer - Paris

💡 Partenaire de la transition
Stage
Localisation Paris, France
Télétravail ponctuel autorisé
A partir de 0 année(s) d'exp.
Publiée le 29/07/2024

Iceberg Data lab

Iceberg Data Lab provides environmental data solutions to financial institutions, mostly by computing climate and biodiversity impacts of companies.

💡 Partenaire de la transition

La mission de cette structure est d’aider les entreprises ou les citoyens à améliorer leur impact environnemental et social. Par exemple le conseil en RSE, la formation, la sensibilisation aux enjeux de la transition, les médias,…

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Mesure d'impact
Iceberg Data lab n'a pas encore transmis de mesure d'impact
Labels et certifications
Cette structure n'a pas souhaité nous communiquer les labels ou certifications qu'elle a pu obtenir.
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To compute the most relevant environmental scores (like the 2° aligment and the biodiversity impact) of every company, we need a rich database, relying of multiple sources - often unstructured -, an efficient impact computation and frequent deep analyses on the quality of our scoring.

Among the ~35 companies employees, the Data Team (4 people) is part of the IT team (12 people) and is looking an intern to:

  • Improve our data extractor pipeline which leverages Large Language Models (LLM) APIs and models (and a bunch of other nice tech bricks) to extract automatically the right data into the right format.
  • Search and collect new data sources (GHG emissions from companies, assets, natural protected areas, tons of products produced, consumed, companies facilities, etc.)
  • Automatically detect discrepancies between the default model and all refinements brought by our analysts.
  • Handle our big company database and its evolution in time.
  • Build new products that are made possible with LLM
  • Benchmark our scores against academic environmental papers.
Profil recherché

Minimum: Python, Data Science, data analysis, data engineering, Pandas, SQL, Git, interest in environmental issues.

Optional: Machine Learning, Docker, Langchain, Ollama, Airflow, FastAPI, ElasticSearch, Streamlit, Spark, Kubertnetes, Celery, Ruby, Financial or environmental knowledge.