Quantitative Research Developer - Remote

As a Quantitative Research Developer at SCM, your primary responsibilities include designing, developing, and supporting simulation frameworks for backtesting execution approaches. You’ll collaborate with other quantitative researchers to brainstorm and develop new trading ideas leveraging your technical skills.

To qualify for the Quantitative Research Developer role at SCM, candidates should have proficiency in C++ and Python, along with experience in research, building, and maintaining trading systems using market data. A strong understanding of data paths from tick to trade and experience analyzing time series data are also important qualifications.

SCM offers a flexible work environment for the Quantitative Research Developer position, allowing you to work in-person, remotely, or in a hybrid setup. This flexibility is designed to promote work-life balance and accommodate individual preferences.

SCM is committed to fostering a diverse and inclusive workplace for all its employees, including those in the Quantitative Research Developer role. This commitment is reflected in their practice of valuing and promoting equal employment opportunities, ensuring every team member feels heard and engaged.

The anticipated base pay for the Quantitative Research Developer position at SCM ranges from $150,000 to $300,000 per year. This role also offers additional forms of compensation, such as bonuses and a comprehensive benefits package.

When answering this question, share specific projects where you utilized C++ and Python to develop trading systems or statistical analyses. Highlight how your coding skills contributed to the effectiveness of your work and provided solutions to real-world trading scenarios.

Discuss your approach to backtesting, including the frameworks you've developed and the metrics you consider essential for analyzing performance. Be sure to mention any experience you have with simulation frameworks, particularly within the microstructure of markets.

Focus on your experience working with large datasets in your previous projects. Talk about the tools and techniques you employed to efficiently manage, analyze, and derive insights from such data, and the importance of accuracy and efficiency in your analyses.

In your response, explain the concepts of market microstructure, such as liquidity, pricing, and transaction costs, and discuss how an understanding of these factors can aid in developing effective trading strategies. Mention any experiences where you applied this knowledge.

Share a detailed account of a trading idea you've influenced, highlighting your thought process, the data you analyzed, and the impact it had on trading outcomes. Emphasize collaboration with team members and how the idea integrated with existing systems.

Explain the specific tools, libraries, or methodologies you use for time series analysis. Mention any statistical techniques or machine learning algorithms that you've employed to analyze financial data more effectively, providing a thought process for identifying patterns.

Your answer should detail your systematic approach to tackling complex problems, including gathering data, formulating hypotheses, and testing solutions. Give examples of challenges you've faced and how your critical thinking skills led to successful outcomes.

Articulate how effective communication is vital in conveying complex quantitative concepts to stakeholders. Provide examples of how you've successfully communicated ideas and findings to diverse audiences and the impact this had on projects.

Discuss trends you see emerging in quantitative research within trading, such as advancements in machine learning or AI, and speculate on future developments. Highlight your eagerness to stay updated with industry changes and how you'd adapt to them.

Focus on your time management techniques and prioritization strategies when addressing multiple tasks. Illustrate your ability to adapt to changing circumstances and maintain productivity, even under pressure, sharing examples from your past work.