Balyasny Asset Management L.P. (BAM) is a global institutional investment firm. BAM’s culture, technology, and platforms empower our investment teams to perform at their very best. By hiring a diverse group of ambitious, innovative, and deeply knowledgeable individuals from all backgrounds, we are able to generate more creative and profitable investment opportunities for our clients.
BAM exists at the intersection of finance and technology, combining the deep industry knowledge of leading portfolio managers and financial analysts with software engineers and quantitative researchers. We leverage the collective expertise of our teams to seek out new investment opportunities, analyze market conditions, minimize risk, and provide superior service to our investment partners.
With 1700+ people in offices around the world, we embrace a culture that welcomes the free flow of ideas, promotes career development, and supports the health and wellbeing of our people through world-class benefits.
ROLE OVERVIEW
Responsibilities include, but are not limited to:
- Research on alternative data while working alongside a Commodities Portfolio Manager
- Communicating with data providers and inhouse data analysts
- Building and maintaining full stack applications
- Quantamental analysis on the commodities market, including but not limited to, bulks, metals, industrials
REQUIREMENTS
· Bachelor’s degree or the equivalent in Business, Business Administration, Finance, Economics, Mathematics or Engineering from a reputable university.
· 3 years of experience from firms including but not limited to Hedge Funds, Trading Houses, Prop Desks or Banks
· Experience on quantitative research and signal generation
· Strong machine learning and deep learning capabilities
· Python and SQL based programming skills for data processing and analysis required for end-to-end quant architecture build out - data management and processing, signal backtesting, portfolio construction and execution straight through processing
· Ability to identify and quantify alternative datasets in the commodities space