Quantitative Researcher Intern

Who We Are

Founded in Modena Italy in 2016 Axyon AI marks ten years of applying artificial intelligence to investment management. We develop AI-powered solutions for asset managers and hedge funds leveraging proprietary AI and deep-learning technology to analyse financial time series identify predictive signals and support systematic investment strategies.

Our quantitative researchers data scientists and engineers work together to address the challenges of applying AI to financial markets from evaluating signals to building and refining investment strategies. For an intern this offers the opportunity to learn how rigorous quantitative research is carried out and to see how research findings can contribute to real investment solutions.

We support professional development and offer flexible working arrangements within an inclusive collaborative team.

What Were Looking For

We are looking for a motivated Quantitative Researcher Intern with a strong technical foundation intellectual curiosity and a genuine interest in quantitative finance machine learning and systematic investing.

You will join our Quant Research team and work with close guidance from experienced colleagues on a focused research project investigating how predictive signals can be combined and integrated into systematic investment strategies to improve robustness diversification and alpha generation.

The internship is open to candidates seeking either a curricular or extracurricular placement. Duration will be agreed with the selected candidate.

Location: Modena Italy

Expected start date: JanuaryFebruary 2027

What Youll Be Doing

Contribute to a well-defined research project within the Quant Research team:

• Review relevant academic literature and internal research on signal evaluation signal combination and systematic strategy design.
• Prepare clean and explore financial datasets with support from the team.
• Implement and compare approaches for combining predictive signals from transparent baseline methods to more advanced statistical or machine-learning techniques where appropriate.
• Evaluate signal quality and robustness using sound statistical methods and out-of-sample analysis while learning to recognise common sources of bias and overfitting.
• Study how combined signals can be integrated into systematic strategies and assess their impact on performance risk stability turnover and transaction-cost sensitivity.
• Develop reproducible Python notebooks scripts and research outputs documenting assumptions methods results and limitations.
• Discuss progress regularly with the team and present findings clearly at the end of the project.

What You Need

• Currently pursuing or having recently completed a Bachelors or Masters degree in a quantitative discipline such as finance economics or a STEM subject.
• A solid academic foundation in quantitative subjects and working knowledge of Python for data analysis; familiarity with libraries such as pandas or NumPy is welcome.
• A curious and inquisitive mindset: you enjoy asking questions exploring unfamiliar topics and looking for interesting problems to solve.
• A collaborative mindset: you are a team player who communicates openly shares ideas and learns from feedback.
• Clear written and verbal communication skills including English proficiency at B2 level or above.
• No previous professional experience in quantitative research is required.

Nice to Have

Experience or familiarity with one or more of the following is considered a plus:

• Coursework a thesis or a personal project involving machine learning quantitative finance econometrics portfolio theory or financial time series.
• Basic familiarity with systematic investing signal validation portfolio construction or backtesting.
• Experience with Git-based version control SQL or reproducible research workflows.
• Evidence of technical curiosity through academic open-source or independent projects.

What Youll Gain

• A monthly internship allowance ranging from 500 to 800.
• Flexible working hours to support your academic commitments.
• A supervised research project with clear milestones regular feedback and hands-on experience applying machine learning to financial markets.
• Mentorship from quantitative researchers data scientists and engineers with opportunities to present and discuss your findings.

If you are passionate about technical problem-solving eager to learn and curious about how quantitative research becomes a systematic investment strategy we would love to hear from you!