Quant Research Intern — Build Real Trading Models

Tradermath; Amsterdam; On-site; EUR 25,000 - 32,000; Full time

Job summary

Graviton is seeking a Quantitative Research Intern for 10-12 weeks to work on cutting-edge market research and trading strategy development in Amsterdam. You will investigate market behavior, build predictive models and solve open questions alongside researchers and engineers.

You’ll design experiments, backtests and production-ready signals, applying ML, probability and optimization to real data while collaborating across offices. Strong math and programming background required.

Qualifications

• Pursuing full-time undergraduate, Master's, or PhD in a quantitative discipline with graduation in 2028
• Strong Python and/or C++ programming ability
• Solid grounding in probability, statistics, linear algebra and optimization; ML exposure
• Experience with ML, statistical modelling or data analysis from coursework, research, or projects
• Comfort with large, complex datasets and strong analytical thinking
• Excellent communication skills to convey complex ideas clearly

Responsibilities

• Work on real research problems and discover predictive patterns from large market data
• Design statistical models to identify trading opportunities
• Apply probability, statistics and ML to open research questions
• Build AI-powered research tools and accelerate experimentation
• Develop infrastructure for large-scale research and backtests
• Translate research insights into production-ready trading signals
• Collaborate with researchers and engineers with regular feedback and mentorship

Skills

Python

C++

Probability

Statistics

Linear Algebra

Optimization

Machine Learning

Data Analysis

Communication

Education

Undergraduate/Master/PhD student in quantitative field

Tools

None

Job description

Graviton is seeking a Quantitative Research Intern for 10-12 weeks to work on cutting-edge market research and trading strategy development in Amsterdam. You will investigate market behavior, build predictive models and solve open questions alongside researchers and engineers.

You’ll design experiments, backtests and production-ready signals, applying ML, probability and optimization to real data while collaborating across offices. Strong math and programming background required.