Senior Quantitative Researcher – Machine Learning Trading

Amsterdam

We are expanding our research organization and are looking for an experienced Senior Quantitative Researcher to join our Amsterdam-based team. This position plays a key role in shaping new research initiatives and carries responsibility for technical leadership, mentorship, and team development.

In this role, you will work on the discovery and productionization of machine-learning-driven trading strategies, while helping guide junior researchers and collaborating closely with trading, engineering, and infrastructure teams.

Your role

1. Creating, testing, and deploying machine-learning-based statistical arbitrage strategies in live trading environments
2. Identifying new sources of alpha through deep analysis of high-frequency market data
3. Developing features and predictive models across large universes of instruments over short-term horizons (seconds to minutes)
4. Building and maintaining scalable ML research and production pipelines across multiple venues and markets
5. Continuously improving existing models through rigorous evaluation and optimization
6. Acting as a key point of contact between research, trading, and engineering groups
7. Providing technical guidance, mentorship, and—where appropriate—line management to other researchers

What we are looking for

1. Five or more years of hands-on experience in a high-frequency or ultra-low-latency trading environment
2. Strong track record applying machine learning techniques to systematic trading strategies
3. Practical experience operating ML pipelines across multiple exchanges, both independently and in coordinated setups
4. Demonstrated ability to design and implement complex statistical arbitrage strategies across multiple instruments
5. Willingness to deepen expertise in high-frequency market dynamics and scale strategies across many trading venues
6. End-to-end ownership mindset, from raw data ingestion and feature engineering through to live deployment
7. Strong programming skills in at least one language; Python experience preferred
8. A self-driven, resilient, and highly engaged approach to problem solving
9. Ability to collaborate effectively with colleagues from diverse technical and academic backgrounds

Nice to have

1. Prior experience mentoring researchers, supervising interns, or leading small quantitative team