Quantitative Researcher EMEA (F/M/D)

As a Quantitative Researcher EMEA at Flowdesk, you will develop and implement quantitative models and algorithms to identify trading signals and enhance profitability. You'll conduct in-depth research on market trends and trading behaviors, build robust backtesting frameworks, and present your findings to stakeholders. Emphasis is placed on collaboration with traders, developers, and data scientists to ensure effective integration of research outputs.

To be considered for the Quantitative Researcher EMEA role at Flowdesk, candidates should possess a Master’s or PhD in a quantitative field such as Mathematics, Statistics, Physics, or Finance. Additionally, a minimum of 3 years of experience in quantitative research or algorithmic trading is required, along with strong programming skills in Python or R.

The Quantitative Researcher EMEA at Flowdesk should have strong programming skills in Python or R. Familiarity with data manipulation and analysis libraries such as Pandas and Numpy is essential, along with knowledge of machine learning techniques for strategy development.

At Flowdesk, the work environment for a Quantitative Researcher EMEA is collaborative and fast-paced, promoting innovation and continuous learning. We foster strong communication among team members and value a proactive and curious attitude, which is essential for success in this role.

Flowdesk provides a range of attractive benefits for Quantitative Researchers EMEA, including 100% health coverage and top-of-the-line equipment. Employees can also look forward to team events, offsites, gym memberships, and opportunities for international mobility as part of a vibrant and engaging company culture.

Be prepared to discuss a specific quantitative model you created, focusing on the thought process behind its development. Mention how you identified the problem, the data used, the algorithms implemented, and the impact it had on trading strategies. This showcases your expertise and problem-solving skills.

Talk about the importance of backtesting and validation in quantitative research. Explain your process for evaluating model performance using historical data, as well as any techniques you use to avoid overfitting and ensure accuracy and reliability in your trading strategies.

Detail your familiarity with machine learning applications in quantitative research. Describe any projects where you utilized machine learning algorithms, the types of models you worked with, and how they contributed to successful trading strategies.

Express your strategies for staying informed in the rapidly evolving crypto landscape. This could include attending conferences, following influential thought leaders in the space, participating in online forums, or leveraging news aggregators to stay ahead of market trends.

Share a specific example that highlights your problem-solving skills and resilience. Focus on the challenge you encountered, the steps you took to address it, and what you learned from the experience, emphasizing the importance of continuous improvement in research.

Discuss your approach to collaborating with traders, developers, and data scientists. Share examples of how you’ve effectively communicated complex ideas to diverse audiences and fostered teamwork to bring research initiatives to fruition.

Point out your proficiency in Python or R, and explain how you've used these languages in your quantitative research work. Provide examples of specific libraries or frameworks you've worked with and the types of projects they were applied to.

Talk about your expertise in data analysis, emphasizing familiarity with libraries like Pandas and Numpy. Illustrate how you’ve applied these skills in past projects, showcasing how effective data analysis has enhanced your decision-making and model performance.

Reflect on your passion for quantitative research and finance, citing specific elements that excite you, such as the challenges of algorithmic trading or the potential for innovation in the cryptocurrency sector. Sharing personal stories can make your answer more relatable and authentic.

Outline your strategies for maintaining clarity and focus in high-pressure situations. Highlight your analytical mindset and experience in managing multiple projects, explaining how you prioritize tasks while ensuring quality outputs in your quantitative research.