Providing best-in-class financial data for our Machine Learning models
Driving the processes behind our rigorous approach to data quality
Screening, evaluating, and expanding our data sources, such as covering additional equity universes or including alternative data sources
Identifying data issues and driving improvements together with our Machine Learning, Quantitative Finance, and Software Engineering teams
Handling communication with our various data vendors to discuss and resolve any problems with their data
Anforderung
A company culture that is based on a spirit of cooperation and is characterized by a high level of quality awareness, openness and attention to detail in all areas of our work
To date Ultramarin is one of the leading drivers for AI-based analyses and decision-making processes in asset management. Ultramarin has been founded in 2017, is headquartered in Berlin, with additional locations in Frankfurt and Munich
Ability to understand and work with large relational databases using SQL
Internal workshops, unique learning possibilities across a wide range of domains as well as amazing team events
Flexible working hours, 28 days paid leave and a competitive salary and equity package
Researcher
Verantwortlichkeiten
Develop Forecasting Models: Design and implement models to forecast the cross-section of stock returns and identify signals for international capital markets
Enhance the Quantitative Investment Process: Contribute proactively to the ongoing enhancement of our data-driven quantitative investment process
Adapt Machine Learning Models: Adapt advanced machine learning models to forecast the cross-section of stock returns
Leverage Data and Computing Resources: Utilize vast amounts of structured data and powerful computing resources to support your research
Refine and Validate Ideas: Use cutting-edge backtesting and performance analysis tools to refine and validate your research ideas
Conduct Experimental Research: Conduct experiments to evaluate hypothesis-driven research on predictive signals
Anforderung
Experience with financial econometrics and cross-sectional asset pricing is a bonus
Strong programming skills in Python, with hands-on experience using libraries for machine learning, data analysis, and financial modeling . Experience with Polars is a plus
A company culture that is based on a spirit of cooperation and is characterized by a high level of quality awareness, openness and attention to detail in all areas of our work
Internal workshops, unique learning possibilities across a wide range of domains as well as amazing team events
The ability to write maintainable and well-tested code using tools such as Pydantic and Pytest
Software Engineer
Verantwortlichkeiten
Build and Maintain Research Infrastructure: Design and implement scalable software systems to support large-scale financial research and backtesting
Simplify and Streamline the Codebase: Refactor existing code to reduce complexity, enhance readability, and ensure consistency, facilitating easier maintenance and scalability
Enhance Model Explainability: Create tools for interpreting and visualizing model outputs, making complex insights accessible to both technical and non-technical stakeholders
Communicate Technical Concepts: Simplify complex ideas and present them clearly to non-technical stakeholders
Anforderung
Strong programming skills in Python, with hands-on experience using libraries for machine learning, data analysis, and financial modeling . Experience with Polars is a plus
A company culture that is based on a spirit of cooperation and is characterized by a high level of quality awareness, openness and attention to detail in all areas of our work
Internal workshops, unique learning possibilities across a wide range of domains as well as amazing team events
The ability to write maintainable and well-tested code using tools such as Pydantic and Pytest