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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.
Job Responsibility
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 specifications that bridge the gap between investment intent and algorithmic execution
Collaborate across technical workstreams to ensure research outputs are aligned with investment objectives and successfully integrated into production workflows
Requirements
Degree in Finance, Quantitative Finance, Financial Engineering, Mathematics, or a related field
Understanding of portfolio construction, asset allocation, and risk management
Solid Python/Java programming skills, with experience in financial modeling, data analysis, and working with ML-driven workflows
Experience interpreting, validating, or stress-testing quantitative or machine learning models (e.g., backtesting, scenario analysis, or model diagnostics)
Ability to bridge finance and technology: translate investment concepts into technical requirements and challenge model outputs using real-world financial intuition
Strong analytical mindset with the ability to communicate complex quantitative insights clearly to both technical and business stakeholders
Fluent in English (written and spoken)
Nice to have
Solid understanding of Git-based workflows (branching, code reviews, version control) in collaborative research or production environments
Experience leveraging LLMs and AI coding tools (e.g., Claude Code, GitHub Copilot) to accelerate prototyping, refactor code, and optimize algorithmic performance
Proven ability to thrive in high-pressure, collaborative environments, delivering precise results under tight market-driven or project deadlines