Role Overview
We are seeking a Data Scientist to join our Research and Development Team in Milan.
This role is designed for someone who thrives at the intersection of advanced mathematics, machine learning, and scalable coding.
Working within a collaborative team of scientists and engineers, you will focus on developing the AI models that are at the core of everything we do. You’ll be joining a mission where we have been pioneering and evolving these proprietary models for more than a decade, driving the innovation that powers our fintech solutions.
Whether you are a seasoned researcher or a brilliant profile with a passion for AI, your contribution will directly impact our investment strategies and internal modeling tools.
We are looking for a creative problem solver who thrives on technical challenges. You don't necessarily need to be a finance expert, but you must be curious enough to master how financial data behaves.
Key Responsibilities
- Design, build, and optimize end-to-end AI pipelines for predictive and prescriptive analytics in financial markets, leveraging modern techniques such as large language models (LLMs), statistical modeling, and probabilistic methods. This includes proficiency in integrating LLMs into research workflows and orchestrating data pipelines.
- Formulate hypotheses, identify intermediate milestones, and meet deadlines for long-term ambitious research goals.
- Own the full model lifecycle, from data selection and representation to deployment and performance tuning.
- Develop and execute rigorous validation frameworks (e.g., backtesting, walk-forward analysis) to ensure robustness and statistical reliability.
- Conduct in-depth research by formulating testable hypotheses and driving progress toward long-term R&D goals.
- Build and maintain automated systems to monitor data quality, model health, and detect drift or instability.
- Collaborate with other technical teams to ensure seamless integration between data pipelines and modeling logic.
- Research and deliver insights related to risk exposures, portfolio construction, and quantitative analysis of the investment process
- Apply "divergent thinking" to overcome technical bottlenecks and optimize data retrieval and processing workflows.
Requirements
- Degree in Mathematics, Physics, Machine Learning, Computer Science, or other quantitative fields.
- Python skills are required to independently design and implement solutions.
- Experience using LLMs to accelerate development is welcome, alongside critical thinking to rigorously test and validate generated code for correctness and robustness.
- Strong grasp of programming logic and software development principles.
- Experience with statistical modeling and techniques such as bagging, random forests, hyperparameter optimization, time series analysis, and signal processing.
- The ability to approach problems from different angles and find creative, efficient solutions.
- Fluent in English (both spoken and written).
Bonus Points
- Professional experience or proficiency in Java.
- Ability to understand financial data concepts (prices, yields, corporate actions) to ensure technical structures align with financial reality.
- Experience with SQL/NoSQL databases.
