Working Student – Quantitative Research (m/f/d)

About YUKKA

YUKKA turns unstructured news into measurable signals for financial institutions. We hold [20] years of quantified news data and deliver it through a cockpit, an API, reports and agents to risk, investment, ESG and credit teams at banks and asset managers.

The role

You will join our Quantitative Research team part-time and work on real research problems alongside our quant analysts. You will test ideas from academic research on our data, explore how news sentiment relates to returns and risk, and help keep the data behind our models clean and complete. You will see how systematic investment and risk models are built and evaluated, and you will own well-scoped pieces of work.

What you will work on

• Preparing and validating financial-market and sentiment datasets
• Reading academic papers, forming new hypotheses from them and testing them on YUKKA’s infrastructure
• Exploratory research on how sentiment behaves across sectors, sources and regions
• Finding data quality issues in how we map companies, sectors and sources, and proposing fixes
• Integrating external data feeds into YUKKA’s infrastructure
• Improving and documenting our Python research codebase

What we are looking for

• You are enrolled in a Master’s (or advanced Bachelor’s) programme in finance, statistics, economics, mathematics, data science or a related field, for at least the next 12 months
• Good Python for data analysis and comfortable work with Git; you have handled real, messy data, not just tutorials
• A solid grounding in statistics and econometrics, and genuine interest in financial markets
• Careful and curious: you double-check results and ask why before moving on
• Clear written and spoken English
• Availability of 20 hours per week during the semester, with more possible during semester breaks

What we offer

• Hands-on experience in quantitative research with mentoring from the team
• Working hours that fit around your studies and exams
• The equipment you need: laptop, monitors and whatever hardware and tools your research calls for

How we work

We value empathy, thoughtfulness and learning from our colleagues and our mistakes. Real innovation takes time, hard work and attention to detail, and we give ourselves room to think about hard problems. We are AI-first in practice, not slogan. Our team language is English, German is a plus.

Apply

Apply with your CV and current transcript of records and a short note on a data project you worked on.