Internship: Quantitative Investment Researcher

**About the position**:
We are seeking an ambitious and driven intern to join our dynamic team in Berlin. In this role, you will collaborate with top-tier quantitative researchers, machine learning experts, and software engineers to advance our quantitative investment process.

**Your Mission**:

- **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.
- ** Collaborate with Cross-Functional Teams**: Work closely with quantitative researchers, machine learning experts, and software engineers to understand and address both technical and business requirements.

**Your Skillset**:
To thrive in this role, you should possess:

- A degree in a quantitative field such as Software Engineering, Mathematics, Physics, Computer Science, Data Science, or Statistics.
- Strong programming skills in Python, with hands-on experience using libraries for machine learning, data analysis, and financial modeling (e.g., Scikit-Learn, SciPy, NumPy, and Pandas). Experience with Polars is a plus.
- Experience in collaborative Python development, including proficiency with tools for version control (Git), shell scripting (Zsh/Bash), and continuous integration (Bitbucket/GitHub).
- The ability to write maintainable and well-tested code using tools such as Pydantic and Pytest.
- A solid understanding of machine learning and applied statistics.
- Experience with financial econometrics and cross-sectional asset pricing is a bonus.

**Why us?**:

- An exciting and involving role with significant impact on shaping the future of AI-based sustainable investing
- 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
- The opportunity to work in an interdisciplinary team with our experienced quant finance, software engineering and machine learning experts
- A modern loft office in Berlin, Prenzlauer Berg, healthy food (including coffee, juices, fruit, vegetables, sandwiches) and high-end hardware from Apple
- Internal workshops, unique learning possibilities across a wide range of domains as well as amazing team events
- Benefits such as Urban Sports Club or Egym Wellpass as well as corporate benefits

**About us**:

- 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. Ultramarin is embedded in the global AI community, through a close exchange with leading universities and as a member of Inquire Europe.

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, and is backed by leading international business angels and VCs.