Quantitative Researcher Intern
Confirmed on the employer's own hiring board on Nov 8, 2026. First seen by Alion on Nov 6, 2026.
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 We’re 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: January-February 2027
What You’ll 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 Bachelor’s or Master’s 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 You’ll 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!