Belvedere Trading is a leading proprietary trading firm with offices in Chicago, New York, Boulder, and Singapore. Our traders work hard to provide liquidity to the market through their market-making activities and are the masters of a diverse set of commodities, interest rates, exchange-traded funds (ETF), and equity index options. From the beginning, we began iteratively investing in our proprietary technology and committing to building our systems from the ground up. Our trading models and software systems are continually re-engineered, optimized, and maintained to stay on top of the industry. We operate best within a team environment where we challenge each other to deliver value consistently and share in the success of the company.
Riverside Capital Markets (RCM) is a joint venture between our five founding partners and Belvedere Trading. Our teams specialize in Market Making and Quantitative Trading in Equity & ETF Options, ETFs, Futures and Futures Options. We have teams based in New York, Chicago and remote locations that utilize both internal and external software solutions for trading. We look for savvy individuals and teams who are looking to help build great businesses in the trading landscape.
Our quantitative team, working with both tech and trading, are charged with developing scalable algorithms and translating trading models into our extensible systems. We are looking for highly skilled and experienced individuals to join this research initiative, collaborate with the team, and build the next generation of our trading system.
The ideal candidate has direct experience in the quantitative research process and understands the workflow from idea generation to implementation. This position is accountable for making a significant impact on the direction and productivity of a newly formed team within the firm.

• Contribute meaningfully to group direction and project selection
• Research, validate and implement new valuation models based on internal data and external papers
• Utilize machine learning techniques to conduct data analysis
• Apply machine learning techniques to understand market movements
• Conduct financial data analysis on large datasets
• Contribute to in-house data analysis packages and research framework development
• Acquisition of new data sets and cleaning/maintenance of new and existing data sets