Dr. Sebastian Sternberg

Dr. Sebastian Sternberg

Senior Data Scientist // Data Science Consultant

KPMG Wirtschaftsprüfungs AG


I am a Senior Data Scientist working at KPMG Lighthouse, KPMG’s Center of Data & Analytics in Germany. I mainly work on Natural Language Processing (NLP) topics, especially on information extraction from documents, e.g. invoices, contracts or form sheets. I also like to serve as a translator between non-technical stake-holders and the tech team.

Before, I completed my PhD in 2019 at the Graduate School of Social Science and Economics at the Chair for Quantitative Methods at the University of Mannheim. During my PhD, I mainly worked on the application of machine learning approaches to political science questions, with a particular focus on predictive modelling and text analysis, e.g. predicting constitutional court decision-making with machine learning and measuring vagueness in judicial texts.

  • Natural Language Processing
  • Machine Learning
  • Information Extraction
  • PhD, Chair of Quantitative Methods, 2019

    University of Mannheim

  • Double Degree MA Public Administration, 2015

    University of Konstanz & Science Po Grenoble

  • BA in Political Science & Public Law, 2013

    University of Mannheim

Professional Experience

Senior Data Scientist Consultant
KPMG Lighthouse Germany
Oct 2019 – Present Baden-Würtemberg

Responsibilities include:

  • Team lead NLP
  • Information Extraction using NLP from documents (OCR, machine learning, rule-based methods)
  • Text classification using machine learning
  • Stake-holder communication and PMO
  • DevOps, Git, Kubernetes
  • Python, R
Instructor Machine Learning Workshops
Sep 2015 – Present Frankfurt
Taught serveral workshops for machine learning at the German Bundesbank
Doctoral Student & Research Associate
University of Mannheim
Sep 2015 – Aug 2019 Mannheim

Responsibilities include:

  • Teaching statistic classes for undergraduate and graduate students
  • Doctoral thesis title: No Public, No Power? Analyzing the Importance of Public Support for Constitutional Review with Novel Data and Machine Learning Methods
  • Publication of the Year Award for paper “How cross-validation can go wrong and what do to about it”
  • Presenting results at international conferences

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