In this role, you will be part of our equity selection squad, which develops holistic equity selection models to predict the cross-section of stock returns in the financial markets.
The equity selection squad is characterized by a high-performance culture where collaboration, transparency, and meritocracy are paramount.
As part of the squad, you are developing and testing your ideas with real world data, leveraging our cutting-edge backtesting and performance analytics capabilities.
This is supported by large amounts of (clean) data and enormous computing power at your fingertips. While we start with the standard ML toolkit to get a model running, we strive to understand what's going on under the surface, because an off-the-shelf solution doesn't lend itself to our use cases.
Joining the squad, you will contribute to the development and continuous improvement of our data-driven systematic investment strategies.
This includes conducting experiments and evaluations of our hypothesis-driven research agenda, identifying, confirming, and amplifying predictive signals in our data while filtering out irrelevant information.
To this end, you will work closely with our experienced quant finance and machine learning experts.
We are a deep tech pioneer, providing AI-based investment solutions to our clients. Building on the best practices of quantitative asset management we tap the potential of machine learning for sustainable investments on capital market.
Our interdisciplinary team consists of experts in the fields of finance, computer science, software engineering, machine learning as well as mathematics, physics, and neuroscience.
Othoz is embedded in the global AI community, through a close exchange with leading universities and as a member of Inquire Europe.
To date Othoz is one of the leading drivers for AI-based analyses and decision-making processes in asset management. Othoz has been founded in 2017, is headquartered in Berlin, Germany and is backed by leading international business angels and VCs.