This opportunity is based in Lausanne

ESG Ratings from publically available Data using NLP (Master Thesis)


The public concern about the climate crisis, glaring headlines about corruption scandals and the growing awareness about precarious working conditions have given rise to ESG ratings. These ratings aim to score companies according to their exposure to environmental, social, and corporate governance risks (ESG). They are increasingly considered as an important economic factor in investments, as a growing number of financial institutions now propose ESG-compliant portfolio investments.

Various rating agencies are specialized in assessing companies’ ESG policies. Some of them make their ratings publicly available, their assessment methods however remain secret.

In this internship, you will conduct a profound analysis of the available ESG ratings based on public data sources. The aim is first, to understand how the scores are computed, and second, to automate the rating process.

You will start your analysis with a previously gathered dataset and, if necessary, expand it with more data later on. To achieve the objective, you will experiment with the whole range of data science techniques, from basic data cleaning methods, to advanced regression and natural language processing (NLP) approaches.

In this role

The goal of the internship is to:

  • Study the literature about ESG risk assessment
  • Design and implement a procedure to characterize companies based on information extracted from non-structured data
  • Investigate approaches to predict a company’s ESG score and find ways to explain the prediction.

What we offer

 Join our team as intern and you will find a young, dynamic and culturally diverse working environment.

About your profile

  • Interest and strong knowledge in Machine Learning (ML), Natural Language Processing (NLP), and explainable ML
  • Experience with Python (NumPy stack, common ML and NLP libraries)
  • Experience with Git, Docker and scraping/crawling is a plus

If you are INTERESTED in applying for this position, please send us your complete application (CV, cover letter, letter of reference, diplomas and certificates).

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