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Quantitative Data Scientist

MDOTM·Salary not specified

Primary stack

PythonJavaMachine Learning

Nice to have's

GitAI

Job description

About the Position

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.

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 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., 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.

We Offer

  • Opportunity to work in a cutting-edge environment with a global team.
  • Competitive salary and benefits package.
  • Professional growth and development opportunities.

About the Company

MDOTM is the global provider of AI-driven investment solutions for Institutional Investors. Founded in London in 2015, the company has a team of over 100 physicists, data scientists, engineers, and finance professionals. MDOTM provides Portfolio Advisory and Asset Allocation services to Banks, Insurance Companies, Family Offices, Pension Funds, Wealth and Asset Managers. Financial institutions across the UK, Europe, and the US leverage MDOTM’s advisory and its AI platform to support their investment process.

New York | London | Milan

© MDOTM. This job description was sourced from the employer's public career page. TheJob is not the employer — we index the posting and route candidates to the source. All content rights and hiring decisions belong to the employer.

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