80-120K + OTE, Amsterdam NL
My client is a leading global prop trading house using algorithmic trading and cutting edge technology to buy and sell securities across multiple exchanges worldwide. From their offices in Amsterdam, US and APAC they provide liquidity to the financial markets globally.
Recently they acquired a rapidly growing algorithmic trading firm with the ambition of creating tomorrow’s most technically advanced trading stack. By combining their highly advanced trading strategies with my client’s execution and scaling capabilities, this move strengthens their position in the market and is an important step towards ensuring stability and long-term growth for the firm. As a result of their growth ambition they are looking to hire an exceptional Quantitative Researcher.
They build robust, fully automated systems used to predict and trade cryptocurrency markets, with an ambition to scale way beyond crypto to markets such as equities. As a technology and data-driven team, they design and build their own cutting-edge systems, from high-performance trading platforms to large-scale data analysis and computation infrastructure.
As a Quantitative Researcher, you will be operating as a senior and core member of their high-performance, dedicated, multi-disciplinary team, with a background in math, statistics, robotics, quantum chemistry, data science and beyond. Most importantly, you’ll make a real impact on the architecture and quality standards of their tech infrastructure.
You will make a real impact on their machine learning models, execution algorithms, tech infrastructure, and R&D quality standards generally. You will have full ownership of the design and development of models and algorithms responsible for trading and optimal execution of close to $3bn in monthly volume.
They are looking for a creative scientifically minded individual who can lead the growth of the firm’s quant research to improve prediction quality in delta 1 mid-frequency (intraday) trading context. You’ll make a real impact on their alpha generation, feature modelling, machine learning ensembles, alternative data research, and R&D quality standards generally.
Amongst others, you will work together with other quants tackling challenging problems such as optimal execution, alpha- and risk modeling, and optimization problems: from signals to trading strategies, develop predictive ML frameworks, analyze order book/trades data for execution models, and build market impact models.
You will develop and improve sampling-, weighting-, cost functions for model training and also develop Machine Learning/Network architectures (MLP,CNN,GNN) for custom prediction in trading, including data representations.
Within a complex landscape of varying systems and technologies, you’ll be constantly challenged to conduct original and computationally intensive research in an autonomous fashion, applying your knowledge in Machine Learning, probability, (Bayesian) statistics, (non-convex) optimization, stochastic control and mathematical modeling.
Below is what you will bring;
- An advanced degree with excellent grades in Mathematics, Physics, Computer Science, Data Science, Engineering, Statistics or any other highly quantitative field
- Experience as Researcher in (mid-frequency / intraday) D1 predictive modelling, developing orthogonal features across multiple categories in statistical arbitrage context.
- Significant practical experience with at least one mainstream ML (XGB, (C/R)NN, LSTM, RF, …) approach to improve prediction quality in a trading context, being aware of all ways one can overfit in the process. Solid knowledge of probability, (Bayesian) statistics, (non-convex) optimization, and mathematical modeling more generally
- Experience extracting predictive signals (alphas) from both public feed and alternative data.
- Scientific mindset with experience in numerical programming with Python. Detail oriented and highly structured, using a rigorous process to ensure your results are reliable and well tested
- Serious about clean code, simple but well-architected systems, and continuous improvement
- Sense of ownership taking full responsibility and accountability for your contributions
- Technical accomplishments are considered a plus: Kaggle, Hackathons, Olympiads, academic publications in e.g. NeurIPS, ICML.
Below is what you can expect;
Their quantitative researchers generally have a strong academic background in Mathematics, Physics, Computer Science, Data Science, Engineering, Statistics or another highly quantitative field. The one thing they all have in common, regardless of their individual backgrounds, is their creative and scientific mindset.
You will not get a lot of top-down instruction telling you exactly what to do and when to do it. You will get directional advice, useful frameworks, access to interesting challenges, and plenty of freedom to execute as you see fit. They are meritocratic by nature and believe that empowering talent in the organization is the only way to achieve their ambitious goals.
They are united fundamentally by their love and curiosity for innovation in financial markets and technology. They expect you to be versatile, flexible and very creative in order to come up with new and innovative solutions. In return, they give you the freedom to pursue your ideas and implement them right into the production systems.
They hire from the top 1% of the global quant community and they reward accordingly. Beyond this, they provide their Researchers and Developers an extensive range of training and personal development opportunities, as well as the chance to work with the latest technologies.
While they work hard, they also have a lot of fun. As part of the “family”, you’ll get to enjoy the annual company trip and numerous social events throughout the year.
My client has a strong preference for people who bring something exceptional to the table. Innovative Github and outstanding universities/grades will be evidence of this. TU Delft, Eindhoven, Twente, TU Aachen, Munich, Heidelberg, ETH Zurich, EPFL, Oxbridge, French famous engineering schools are obviously interesting educational backgrounds.
My client prefers candidates from a ‘’performance-driven” rather than a “fee-driven” background.
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