Quantitative Trader Intern (PhD 2026)

Quant Blueprint LLC; Amsterdam; On-site; EUR 60,000 - 100,000; Full time

Benefits offered by this job

Quarterly bonus based on performance

Job summary

Quant Blueprint LLC in Amsterdam seeks a Quantitative Researcher to identify trading opportunities and innovate trading strategies. You'll collaborate with diverse academics and contribute to the firm's success from day one.

The ideal candidate has strong quantitative skills, embraces creative problem-solving, and is motivated by financial performance. Familiarity with statistical methods and low-level programming languages is essential for this role.

Qualifications

• Persistent drive to improve and rise to the next level.
• Ability to adapt based on new data, results, and feedback.
• Orientation for making money; focus on value creation.

Responsibilities

• Identify trading opportunities and innovate trading strategies.
• Work on various projects and collaborate with academics.
• Contribute to firm’s bottom line within the first few weeks.

Skills

Creative Problem Solving and Probabilistic Thinking

Strong intuition and deep thinking with data sets

Experience with modern AI techniques

Low-level computer languages like C++, Python

Job description

As a Quantitative Researcher, your focus is on identifying trading opportunities, but you can add even more value with strong quantitative skills and some coding proficiency to accelerate the innovation process and help others leverage your work. By working on a variety of projects with different collaborators over a six-month period, you'll gain new knowledge and insight into the fundamentals of market dynamics, trading strategies, and our proprietary research platform. We've stripped away the boundaries of what you can access and touch internally, so while you learn the intricacies of our industry, you'll have plenty of opportunities to contribute and directly affect our bottom line within your first few weeks on the team. While interest in trading is key, a background in finance is definitely not. Our team is built mostly from academia – professors, postdocs, PhDs, and undergraduates – not from other trading firms. We seek mental diversity and add a select group of academics each year from a wide range of disciplines.

COMPENSATION

Competitive salary, plus quarterly bonus based on individual performance and contribution towards success of others and the firm.

Qualifications

• Persistent Drive to Improve – Do you have an innate desire to rise to the next level, even after great accomplishment?
• Creative Problem Solving and Probabilistic Thinking – You must enjoy learning and implementing new concepts quickly, combining knowledge from different domains to create new ideas, and take a data-driven and probabilistic approach to testing and implementing new ideas.
• Team Mindset – We want people who understand 1+1 > 2 and are as committed to making the team better through sharing ideas as they are driven to improve their individual performance.
• Mental Flexibility and Self Awareness – You'll have to frequently adapt based on new data, results, and feedback on your trading ideas and your performance.
• Orientation for Making Money – Although we value academic training, our work is not an academic exercise. We take a hacker's approach to testing ideas, dropping projects that consume time without high upside, and focusing our next efforts on what will create the most value for the firm.

Research / Quant Trading Strategy Skills to Have or Develop

• Strong intuition and deep thinking with data sets – Designs new alphas, understands complex systems; knows where to start, or asks others where to start.
• Demonstrates strong ability to quickly get into data to look for empirical relationships and decipher noise or signal.
• Familiarity with classical statistical methods and knows when and how to apply them in a rigorous fashion; easily learns how to apply new statistical methods; will seek out and learn new methods to better solve problems. Experience with modern AI techniques and methods or desire to work on applied machine learning problems is a plus.
• Constantly questions finance/trading data and stays motivated to seek answers despite most often proving that there is no correlation or signal.
• Experience in setup of research framework and execution of projects.
• Understanding of financial products, market dynamics, and microstructure.
• Low-level computer languages like C++, Python, Java, etc.; awareness of strength in particular language and ability to solve more complex problems due to understanding nuances of the language.