I am an independent Data Science consultant aiming to help organizations create value using data. Please see the following sections for an overview of what I can offer you and your business. If you are interested in elaborating on new projects, please feel free to contact me via email or LinkedIn.

Workshops & talks

I have organized workshops and given talks for organizations from industries like banking or IT consulting. Topics included:

  • Workflow and tools for ML development: covers theoretical basics and practical examples (mostly in Python) from all stages of the ML development workflow, i.e., explorative data analysis, feature engineering, model development and evaluation, as well as model deployment
  • Ethical AI - State-of-the-art in technical research and practice: covers terminology, algorithmic approaches and practical examples for the areas of algorithmic transparency, fairness, safety, privacy and accountability

I typically tailor depth and duration of workshops/talks to my clients’ needs, ranging from one-hour talks to full-day workshops.

Project work

I am constantly looking for challenging and interesting projects in the area of data science and machine learning. Through my research, study and previous work, I am experienced in the following areas:

  • running real-world data science projects end-to-end, from business case development to deployment
  • training deep neural networks on computer vision and natural language processing tasks using frameworks like TensorFlow, PyTorch and
  • building and evaluating ML models using different types of algorithms and libraries like scikit-learn
  • building full-stack web applications with languages like Go, JavaScript (React, node.js) and databases like PostgreSQL and MongoDB
  • working with container-based systems on cloud infrastructure (AWS, Google Cloud platform, Paperspace Gradient)

Learning new technologies excites me. Currently, I am diving deeper into the following areas:

  • Transparency- and privacy-enhancing methods for ML models
  • Probabilistic programming and Bayesian modeling
  • Deep reinforcement learning