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Point72
Cubist Data Analyst
🌎New York
1w ago

Job Description

ROLE

We are passionate about data. We collaborate to build elegant, effective, scalable and highly reliable solutions to empower predictive modelling in finance.

Cubist’s data services group is looking for a Data Analyst to join our dedicated team. Our group is responsible for the timely delivery of comprehensive and error-free data to some of the most demanding and successful systematic Portfolio Managers in the world.

This exceptional individual will be a member of a small team of Data Scientists/Analysts who play a vital role in ensuring the smooth day-to-day implementation of a large research infrastructure, and the live production trading of billions of dollars of capital across global capital markets, including equities, futures, options and other financial instruments. 

RESPONSIBILITIES 

  • Analyzing financial datasets and preparing reports for investment teams
  • Creating derived data content and data ingestion pipelines
  • Identifying potential data sources
  • Engaging with vendors to understand characteristics of datasets
  • Defining and automating qualitative data checks
  • Assist with data questions and requests from investment teams
  • Creating and maintaining internal documentation on datasets

REQUIREMENTS 

  • Basic level programming experience in Python and SQL. Experience with AWS and Airflow preferred but not required
  • Financial industry experience preferred but not required
  • Strong organization, communication and interpersonal skills
  • Attention to detail and a love of process
  • Strong oral and written communication skills
  • Ability to exercise sound judgment in assessing and determining how to handle queries, calls and issues
  • Ability to multitask and prioritize assignments

The annual base salary range for this role is $100,000-$150,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.