Deep Researcher

Requirements

• At least 2 years of relevant financial research experience
• Proven experience of extracting insights from large and complex datasets using SQL and Python. Experience with alternative datasets a plus.
• Demonstrable financial knowledge - passed at least Level I of the CFA program, or similar financial qualification (i.e., MBA or accountancy background). Thorough understanding about various valuation approaches and methods.
• Expertise in time series analysis and models.
• Proficient in coding in C/C++ and Python.
• Excellent communications skills (both written and oral English).
• Strong analytical and conceptual skills encompassing finance and related areas of financial investment.
• Familiarity with various research/database platforms (Bloomberg, Reuters etc.) would be preferred.

Benefits

Additional Information

WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies - the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. The Role : This is a highly unique opportunity for a Quantitative Researcher to join a new and rapidly growing team. In this role you will partner with a close-knit team of data scientists, data engineers, technologists and data sourcing colleagues to identify and research new sector or broad Alphas based on a deep understanding of fundamentals. Develop a structured and methodical research agenda combining fundamental knowledge, data exploration and quantitative analysis. Become a domain expert on each fundamental topic you cover, identify key information drivers to target your research. Conduct detailed data exploration to acquire a comprehensive understanding of the data used to generate Alphas; enrich a wide range of structured and unstructured data into datasets to enable your quantitative research. Take a process orientated approach to understand the value drivers of your Alphas

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