| Company | Global trading and investment firm American Trading Firm |
| Company Info | A global trading firm with a diversified approach, trading across various asset classes and markets. Focused on technology, research, and risk management, fostering a culture of collaboration, learning, and innovation. Operate out the HQ in APAC. |
| Job Title | Quantitative Research Analyst |
| Job Short Desc | Join as a Quantitative Research Analyst to develop mathematical models, utilize machine learning, and extract predictive signals from financial data. Collaborate, innovate, and impact trading strategies in a dynamic environment. |
| Salary | Variable based on experience |
| Locations | USA - Chicago |
| Visa | Y |
| Job Summary | As a Quantitative Research Analyst, your role involves harnessing mathematical models and machine learning techniques to extract predictive signals from financial data, ultimately shaping automated trading strategies across diverse asset classes. Collaboration with the research team is paramount for idea generation and strategy development, with room for independent exploration and innovation. You'll have access to clean data and computational resources, benefiting from the guidance of seasoned researchers, traders, and dedicated software developers. Your responsibilities encompass quantitative analysis of market data, uncovering relationships, and identifying historical trends. Notably, prior familiarity with financial markets is not a prerequisite; comprehensive training will be provided to ensure your success. If you possess a technical degree with a focus on statistics or machine learning, proficiency in programming (Python, R, MATLAB, or C++), and the ability to advocate your beliefs effectively, this role offers an opportunity to actively contribute to the development of trading solutions and make an impact in a dynamic trading environment. |
| Criteria | | Utilize advanced statistical learning methods (Technology) | | Translate research strategies into production software | | Apply machine learning algorithms effectively |
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| Apply | Apply |