Quantitative Researcher | Trader

As a Quantitative Researcher | Trader at our hedge fund, your main responsibilities will include engaging in the full research cycle of trading strategies. This involves collecting and analyzing market data, formulating and testing hypotheses, backtesting strategies, and monitoring their performance in real-time. You will also collaboratively work with engineers and quants to enhance the trading infrastructure and develop models for market impact and portfolio construction.

To qualify for the Quantitative Researcher | Trader position at our hedge fund, candidates should possess a Master’s or PhD in a quantitative field such as Physics, Mathematics, or Computer Science. A strong proficiency in Python in a Linux environment, along with a deep understanding of statistics, data modeling, and time series analysis, is essential. Additionally, fluency in Russian and English is required.

In addition to the required qualifications, beneficial skills for a Quantitative Researcher | Trader include experience with machine learning frameworks like PyTorch or TensorFlow, knowledge of Airflow and data streaming tools, and skills in high-performance programming languages such as Rust or C#. These skills will help you contribute effectively to the development of scalable and high-performance trading systems.

The work culture for a Quantitative Researcher | Trader at our hedge fund is characterized by innovation, collaboration, and flexibility. You will be part of a dynamic and inclusive environment where your ideas are encouraged. We prioritize professional growth and provide ample vacation time along with mental health support to ensure you can perform at your best.

As a Quantitative Researcher | Trader, you will enjoy numerous benefits, including 35 days of vacation and 100% paid sick leave, so you can recharge and recover without financial worries. You will also have access to top-tier equipment that suits your needs and a corporate psychologist for mental health support. This role offers the unique opportunity to work on impactful projects that drive the fund's success.

In answering this question, start by detailing your process of collecting and analyzing market data, discussing specific tools or methodologies used. Highlight your experience in formulating and testing hypotheses and elaborate on how you've conducted backtesting to evaluate the effectiveness of your strategies. Demonstrating a structured approach will showcase your expertise in this vital area.

For this question, illustrate your familiarity with different statistical methods such as regression analysis, hypothesis testing, time series analysis, and machine learning techniques. Providing concrete examples of how you've applied these methods in past projects will reinforce your answer and show your depth of knowledge.

When addressing this question, discuss the tools and technologies you use for real-time monitoring. Mention any metrics or KPIs you track and how you make adjustments based on performance data. Sharing specific examples of how you've successfully adapted strategies based on monitoring results will strengthen your response.

In your response, narrate a specific scenario where you faced a significant challenge in your research. Discuss the steps you took to identify the problem, the analytical methods you employed to explore solutions, and the outcome of your efforts. Highlighting your problem-solving skills will demonstrate your capability in this role.

Share your experiences with machine learning by discussing the frameworks you've used, such as PyTorch or TensorFlow. Explain specific projects where machine learning was integral, detailing the types of models you implemented and their outcomes. The focus on practical implementation will showcase your relevant expertise.

In your answer, emphasize the importance of effective communication and teamwork. Provide examples of past collaborations, explaining your role and how you contributed to successful outcomes. Highlighting your ability to work in diverse team settings and cross-functional collaboration will resonate well.

Discuss the various backtesting tools and platforms you're familiar with, such as quantitative backtesting libraries or specialized software. Explain your approach to designing backtests, including how you handle data quality and validation. Providing insights into your method will emphasize your practical experience in this essential area.

You should focus on your programming expertise, especially in Python, and its libraries useful for quantitative analysis, such as NumPy, pandas, or SciPy. Discuss additional languages like R or C# that you’ve used and explain the advantages they offer in specific contexts, underscoring your technical capabilities.

Answer this question by detailing your approach to model validation and integrity checks, including techniques like walk-forward optimization and out-of-sample testing. Discuss how you assess risk and adjust for overfitting to maintain the effectiveness of your models, demonstrating your commitment to quality in research.

Talk about your ongoing learning activities, such as attending conferences, participating in workshops, or following relevant publications and thought leaders in quantitative finance. This showcases your dedication to staying current and improving your skills, demonstrating your drive to excel as a Quantitative Researcher | Trader.