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Quantitative Data Scientist

Pubblicato il 16-08-2026 - Mdotm in Milano

About MDOTM

MDOTM is the Global leader in AI-driven investment solutions. ​​Founded in 2015, we earned significant recognition in 2017 as the only European fintech startup selected by Google for its acceleration program in Silicon Valley.
Since then, we have been growing continuously, establishing a strong international presence with offices in London, Milan and New York

If you are looking for a fast-paced environment and you are willing to take ownership, this is the right opportunity for you Join us

Role Overview

We are looking for a Quantitative Data Scientist to join our Research and Development Team in Milan. This is a critical, high-impact hire designed to be the bridge between complex quantitative modeling and real-world financial application.

The ideal candidate combines a strong financial intuition with a "hands-on" attitude. You will be responsible for translating business needs into technical solutions, ensuring our investment strategies are robust, scalable, and aligned with client expectations. You will serve as a key integrator across research,



and investment functions, bringing order to innovation.

Key Responsibilities
- Critically evaluate ML model outputs to ensure alignment with financial theory and real-world market dynamics, while accounting for client-specific objectives, constraints, and investment frameworks.
- Translate research signals into actionable investment strategies and portfolio construction frameworks.
- Collaborate with ML researchers to refine models, incorporating financial domain expertise and contributing to model design where needed.
- Design, prototype, and scale quantitative models using Python and Java, maintaining a high standard for code quality and modularity.
- Contribute to the financial validation layer of the R&D; cycle by developing and maintaining test frameworks that identify inconsistencies and support continuous model improvement.
- Translate complex portfolio objectives into rigorous, testable modeling spe

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