EXPERIENCE

Experienced Professionals

A CAREER IN LONG/SHORT EQUITIES AT POINT72

Long/Short Equity is Point72’s core strategy and its success is dependent upon our sector-based investing teams. Using fundamental research, our research analysts inform the investment strategies of our portfolio managers. Through our Point72 University, you have access to an unparalleled training and coaching curriculum to help foster your success. We offer you a clear path based on your abilities, willingness to work hard, and performance. Join us if you’re looking for a career at the forefront of investing.

WHAT’S REQUIRED

We’re looking for a deeply curious individual with demonstrated technical excellence who thrives in a fast-paced entrepreneurial environment. The ideal candidate is a proactive self-starter, ready to take the lead on projects with minimal oversight. No prior experience in fundamental equities or finance is necessary, however, you should have:

• A bachelor’s degree in computer science, engineering, or a related technical field, or equivalent practical experience.
• Minimum 2 years experience in an engineering role or similar position.
• Strong proficiency in SQL and Python (with an emphasis on Pandas, PySpark, etc.)
• Experience with cloud platforms, Terraform, CI/CD pipelines & Git, Linux, Airflow, Databricks, Spark.
• Experience with data visualization tools and libraries such as Tableau and matplotlib/seaborn.
• Understanding of data warehousing concepts and experience with databases (e.g. PostgreSQL).
• Strong analytical skills and the ability to adapt to a rapidly changing environment.
• A commitment to upholding the highest ethical standards.

As a data engineer embedded in an equity investing team, you will leverage a variety of innovative technologies to drive data-driven solutions and analyses. In this role, you will focus on bridging the gap between fundamental research and technological innovation. You will partner closely with an investment team to help connect fundamental research with data analysis and software solutions. Specifically, you will:

• Work alongside senior data scientists to develop “quantamental” strategies.
• Develop cross-sector quantitative insights to aid in generating investment ideas.
• Create tools and infrastructure to support and enhance the team’s quantitative efforts.
• Maintain and enhance the current codebase and productionize processes.
• Integrate and process Compliance-approved datasets.
• Leverage technologies such as Spark, Terraform, and Airflow in your projects.
• Employ programming languages like Python and SQL to build robust data solutions.