Description
Quantitative Trader
Location: New York, NY - FiDi
Department: Trading
Location Type: IN_OFFICE
Employment Type: FULL_TIME
Human intuition. Machine intelligence. Relentless exploration.
Exceptional trading emerges where human intuition meets frictionless experimentation. Our platform and processes enable traders to rapidly investigate ideas, identify emergent patterns, and convert insights into live strategies. This synthesis creates a flywheel of discovery—the key to our pursuit of excellence.
The Role:
Summit Securities Management LLC seeks a Quantitative Trader to work with a trading team that researches, develops, and trades algorithmic and quantitative strategies across financial markets.
Responsibilities:
- Design investment and trade policies and make recommendations on the execution of uncovered trading opportunities.
- Develop statistical models to uncover new trading opportunities and analyze market data in Python and C++.
- Perform quantitative research and analysis of trading strategies to interpret the data to identify and forecast trading opportunities with a high level of out-of-sample accuracy.
- Implement viable research into production code in low latency, multithreaded C++ programs.
- Iterate on strategies by performing post trade analysis.
- Develop proprietary research tools to facilitate these research processes and workflows.
- Determine pertinent data required to forecast anticipated trading opportunities and market trends.
- Discern and compile relevant data to understand and project investment opportunities and trade policies.
- Monitor trading systems and live strategy performance throughout the trading day by identifying and responding to any system issues and market volatility surges, ensuring we remain operational during market hours.
Requirements:
- Must have a PhD degree or foreign equivalent in Statistics, Mathematics, Engineering, or related field.
- Must have 2 years of experience in the position offered or related position. Will accept pre or post-PhD experience.
- Must have 2 years of experience with:
- US equity market microstructure
- Writing optimized low latency, multithreaded code in C++
- Applying machine learning and statistical modeling techniques to tick level market data
- Generating quantitative trading ideas and conducting research to validate and improve new and existing trading strategies
- Optimize trading strategy performance by thoroughly investigating post-trade data
- Developing research tooling and automating workflows in Python and C++.
We Offer:
At our firm, our people come first. We are committed to building a culture of collaboration, where we support each other through challenges and celebrate our collective successes. We believe that the strength of a modern workplace lies in its diverse workforce—diverse in ideas, cultures, and experiences. We actively foster this environment and take pride in being an equal opportunity employer. In compliance with New York City’s Pay Transparency Law, the anticipated base salary for this role ranges from $152,339 - $152,339 annually, plus performance bonus, and based on experience and qualifications. Please note, this range excludes other components of total compensation, such as bonuses.
We offer competitive compensation packages, 401k matching, gender-neutral parental leave, and comprehensive medical, dental, and vision insurance. While we value flexibility, we believe that in-person collaboration is key to solving complex challenges, which is why we require employees to be in the office 4 days a week. In-office perks include lunch stipends, fully stocked kitchens, happy hours, and a great location with amazing colleagues.
Our top priority is our people. We invest in a culture that promotes togetherness, helping each other through challenges and celebrating each other's successes. We believe that modern workplaces succeed by having diverse high-performance workforces — in ideas, in cultures, and in experiences. We put in the effort to make such a workplace a daily reality and are proud to be an equal opportunity employer.
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