Quantitative Research Intern ll

Qsentia.com is building a next-generation hedge fund platform that integrates reinforcement learning with large language models to power a state-of-the-art portfolio management system. The company focuses on applying advanced quantitative research and scalable AI to real-world financial markets. Its goal is to deliver superior risk-adjusted returns, particularly during periods of market stress and volatility. Team members work at the intersection of finance, machine learning, and software engineering, contributing to an innovative and highly technical environment. This setting offers early-career talent exposure to cutting-edge tools, research, and production-scale systems in quantitative finance.

Role Description

This Quantitative Developer I internship role is a remote position. The intern will support the design, implementation, and testing of quantitative models and trading strategies, including those using reinforcement learning and large language models. Day-to-day responsibilities include coding research prototypes, cleaning and transforming financial datasets, and running simulations or backtests to evaluate strategy performance. The intern will help integrate models into production systems, monitor their behavior, and analyze performance metrics. Collaboration with quantitative researchers, data scientists, and engineers is expected, including participation in code reviews and documentation of methods and results.

Qualifications

• Strong foundation in quantitative disciplines, including Mathematics and Statistics, with the ability to apply concepts to real-world financial data.
• Knowledge of Quantitative Finance and Quantitative Analytics, including familiarity with portfolio theory, factor models, or risk modeling.
• Interest in Trading concepts, such as market microstructure, order execution, and systematic strategy design.
• Proficiency in at least one programming language commonly used in quantitative research (e.g., Python, C++, or Julia) and experience with numerical or scientific computing libraries.
• Comfort working with data pipelines, databases, and version control tools (e.g., Git) in a collaborative development environment.
• Currently pursuing or recently completed a degree in a relevant field such as Mathematics, Statistics, Computer Science, Physics, Engineering, or Finance.
• Strong analytical thinking, attention to detail, and clear written communication skills for documenting models and results.
• Interest in machine learning or reinforcement learning methods applied to finance is highly beneficial; prior project or research experience in these areas is a plus.