Senior Quant Researcher

Primary Skill Set:

Data Engineering: Handling and processing large datasets, working with data pipelines, and ensuring smooth data flow.

Modeling: Building and refining quantitative models for systematic trading strategies.

Alpha Development: Developing strategies aimed at generating alpha (outperforming the market).

Quantitative Researcher: Strong background in quantitative research with the ability to improve and adjust models for live trading environments.

Backtesting: Experience in designing and running backtests to validate trading strategies. Ensuring the robustness and effectiveness of strategies before deployment.

SQL: Strong proficiency in SQL for querying and manipulating large datasets, creating complex queries, and ensuring data accuracy.

Python: Proficiency in Python, especially for data analysis, model development, and automation of trading processes. Key libraries would likely include pandas, numpy, scikit-learn, TensorFlow, etc.

Firm Characteristics

Size:  AUM (Assets Under Management) of $5 billion.

Market/Strategy Focus: Likely involved in high-frequency or systematic trading with short holding periods (5-10 days).

Backtesting Integration: Strong backtesting environment to test trading strategies before implementation in live markets.

Key Responsibilities

Modeling & Data Engineering: Collaborate with a team to build and optimize trading models, ensuring efficient use of data and integration with existing systems.

Backtesting: Design and run backtests on strategies, analyzing performance, and tweaking models for optimal execution.

Alpha Strategy Development: Develop, test, and optimize alpha-generating strategies, employing both traditional quant techniques and modern data engineering approaches.

SQL & Python Utilization: Use SQL for querying financial data and Python for automating processes, model building, and creating end-to-end trading solutions.

Additional Qualifications:

Experience in Backtesting: Ability to design realistic backtests with attention to detail, including risk management, slippage, transaction costs, and market conditions.

Adaptability with Technologies: Must be open to using the latest tools, technologies, and approaches to optimize trading models and workflows.