Morphais is a Quant VC that enables investments into the most exceptional founders through AI. Using a data and technology-driven investment approach, we reduce human error and bias along the entire investment value chain to make investments more accurate, transparent, and inclusive.
We are driven by the conviction that talent is equally distributed. It is the access to capital that isn't and still too often depends on race, nationality or gender. As an interdisciplinary team with a wide range of scientific backgrounds, we are rethinking venture capital as an asset class to make the startup ecosystem a more capital-efficient and inclusive place.
Join our team on the journey to build the next generation Venture Capital firm and shape the next generation of diverse, world-class start-up founders with us!
Your Tasks & Responsibilities
- You will develop next-gen approaches for our core technologies, including scoring and tracking algorithms.
- You will develop new techniques to solve problems in a creative way for which there is not yet a standard solution e.g. how to identify future unicorns.
- You will report directly to our CTO and work closely with our Behavioral Scientists, Tech, Product, and Investment Management Team to bring data-driven decision making to Morphais’ investment process.
What you should bring
- You bring a strong scientific background in the area of machine learning ideally with a MSc or PhD degree (Mathematics, Physics, or Computer Science).
- You have multiple years of working experience in building customized statistical/machine learning models.
- You are able to derive and develop machine learning methods from scratch to roll them out.
- You have profound experience in deep learning models as well as classical methods.
- You are highly experienced in writing code and prototyping models in Python using common frameworks such as Numpy, Pandas, Scikit-learn, Pytorch with working knowledge of writing maintainable, modular code.
- You have great communication, organizational, and team-working skills.
- Experience with explaining and interpreting machine learning models (XAI) and/or responsibleAI/FairML debiasing approaches is a plus.