Two Sigma is a financial sciences company, combining data analysis, invention, and rigorous inquiry to help solve the toughest challenges in investment management, insurance technology, securities, private equity, and venture capital.
Our team of scientists, technologists, and academics looks beyond the traditional to develop creative solutions to some of the world’s most complex economic problems.
We are seeking a dedicated data specialist to join our highly motivated and innovative Foundational Data team. You'll be part of a close knit, dynamic team of passionate individuals critical to the success of Two Sigma's business. Apply today if this sounds like a team you want to be a part of!
You will take on the following responsibilities:
- Analyze and curate a substantial amount of data for thousands of tradable instruments, including stocks, bonds, futures, contracts, commodities, and more;
- Create systematic, automated processes and tools that allow the team to scale and rapidly onboard new datasets;
- Collaborate with business partners, software engineers, and other data analysts to understand business needs and then design and develop reliable data pipelines that produce quality datasets;
- Support the day to day operations of Two Sigma by applying problem solving, pattern detection, and root cause analysis to a wide range of data issues in a fast paced environment;
- Help drive continuous improvements to the team’s data quality procedures and a consistent approach to how data quality is measured, monitored and reported
You should possess the following qualifications:
- Minimum 1 year of experience required; highly preferred 5+ years experience in an environment that prioritizes data quality
- BS/BA with a strong academic record, which includes university coursework in programming and computing systems
- Exposure to hands-on Python and SQL coding in the context of data pipelining in a professional environment. Prior experience with Pandas, Jira, Bash, GIT desired.
- Financial industry experience is strongly preferred.