Data Research and Acceleration Analyst
New York, New York, United States
With the breathtaking pace at which data is growing, and with new types of data coming online and new hypotheses becoming measurable, we continue to face the inspiring and exciting challenge to reveal value in the world’s data. We are seeking intellectually curious and creative analysts who are deeply passionate about data exploration, augmentation, and analysis to join our team.
In this role, you will partner with our close-knit team of quantitative researchers, data scientists, engineers, and data sourcing colleagues to translate data into actionable insights and to accelerate our vibrant research pipeline. Key responsibilities include:
- Identifying, ingesting, and enriching a wide range of structured and unstructured big data into datasets for analysis;
- Operating and extending the data infrastructure platform to deliver production-grade data curation and analysis services;
- Thinking and acting as data integrity managers -- amplifying data quality and completeness with a process-driven approach and measurement dashboards;
- Owning end-to-end data workflows and developing deep domain expertise on the underlying actors and behaviors manifested through data;
- Communicating data-driven analysis and insights in the form of “data studies” that enable our investment management research process.
- A strong academic track record with university coursework.
- 2-5 years of relevant working experience.
- Applied data analysis experience, especially using popular scripting languages (particularly Python) and/or data analysis tools and languages (PyData, R, Julia, Matlab, Tableau).
- Experience with SQL and relational databases.
- Deep intellectual curiosity and passion for data.
- Excellent problem solving, communication, and analytical skills.
- Eagerness to work in an evolving and fast-paced environment.
- While financial industry experience is a plus, we are open-minded in our search for critical thinkers who are passionate about technology and data.