Über die Position

Ultramarin is a quantitative asset manager in Berlin. We run systematic equity (long-short and long-only) and asset-allocation strategies in developed markets.

You'll join the Equity Selection team, which builds machine-learning models to forecast relative stock returns. Your focus will be the alpha signals that power these models — work that combines research, economic intuition, and engineering. We care not only about finding signals that work, but also about understanding why they work. This is hands-on work: what you build drives our live strategies.

Deine Aufgaben

You'll start by building and evaluating individual signals, and your scope will grow as you do.

• Search for and evaluate signals: Look across datasets and investment universes for signals that hold up out of sample, and assess both statistical and economic performance.
• Implement alpha signals: Transform raw, noisy data — prices, trading volumes, fundamentals, text — into robust alpha signals. Build them as small, tested, parameterised nodes in our feature computation graph, while avoiding look-ahead bias.
• Construct composite signals: Combine many correlated signals into a few robust composites per universe, weighting them by uniqueness and information content, and separating genuine stock selection from unintended static allocation tilts.
• Automate the research loop: Build and use LLM/agentic tooling (e.g. in Claude Code) that discovers signals, runs experiment grids, and generates evaluation reports.

Dein Skillset

To thrive in this role, you should bring:

• A master's degree or PhD in mathematics, physics, computer science, financial engineering, statistics, or a closely related quantitative field.
• A solid grounding in statistics and a working knowledge of econometrics, with the ability to reason about noisy real-world data.
• Familiarity with collaborative Python development (Git, code review).
• The ability to write maintainable, well-tested code (we use Pydantic and pytest).
• Experience manipulating data with Polars or pandas.
• A strong interest in financial markets and the quantitative investment process.
• A team-oriented mindset and a preference for in-office collaboration

Nice to have:

• Experience identifying and evaluating alpha signals for systematic or fundamental equity strategies.
• Experience with ML libraries such as scikit-learn, LightGBM, or PyTorch.
• Experience building LLM/agentic tooling and evaluation.
• Experience with numerical, statistical, and MLOps libraries such as SciPy, statsmodels, and MLflow.

Warum wir?

• A research environment where ideas move quickly from prototype to production.
• Mentorship from senior researchers and engineers, with early ownership of your work.
• A collaborative, interdisciplinary team spanning quantitative finance, software engineering, and machine learning, with a shared focus on quality, openness, and attention to detail.
• Internal workshops and team events, Urban Sports Club membership, and access to Corporate Benefits.
• A loft office in Berlin (Prenzlauer Berg), healthy food and drinks, and Apple hardware.