Chris Hammerschmidt

 Quick Responder

Calling sklearn or PyTorch APIs and using pretrained models seems easy, but digging in to develop a deeper understanding of how methods work, when to apply them, and how to push their limits can be daunting and confusing.

Having gone from research to industry, I have successfully guided interns, bachelor/master graduates, and Ph.D. students on their path through the field of machine learning from concepts to applications and back.

Currently, I work as co-founder and principal engineer at an early-stage machine learning start-up coming out of my colleagues and my academic research. I am also very happy to talk about product-market-fit, validation, business models, start-up fundraising and bootstrapping, and all the things related to the pre-seed/seed stage.

Prior to starting my company, I worked as a machine-learning research scientist bringing deep learning methods on tabular data to know-your-customer and anti-money-laundering products in financial institutions.

Before that, I completed my PhD where I developed machine-learning algorithms for reverse engineering software and applied them on software engineering and network analysis tasks.

Throughout my career, I've done commercial data science consulting projects on the side.