Quantitative Researcher - Enterprise Data
New York, NY
Posted Jun 6, 2022 - Requisition No. 93382
We’re Bloomberg Enterprise Data -fast paced, innovative and expanding. We have worked hard and smart to become the $1bn business we are today. We partner closely with our clients, taking time to understand their unique businesses and individual data and technology needs. Our endless selection of datasets, covering all asset types, with multiple delivery technologies and flexible scheduling mean our clients are able to get exactly the data they need, when they need it, in the format they prefer. Without us, they simply can’t operate. Firms that commit to utilizing only highest-quality data can eliminate the data inconsistencies inherent to working with multiple vendors and lower their costs overall. A partnership with Bloomberg Enterprise Data allows just this, giving them strategic advantage.
What’s the Role?
The Enterprise Data Quant Researcher will apply cutting edge machine learning techniques to financial modeling problems by leveraging the large and varied datasets within Bloomberg Enterprise Data.
In this role you will:
- Be responsible for conducting statistical analysis, developing machine learning methodologies, model estimation
- Explore current academia and market best practices in machine learning approaches
- Assess quality controls around different approaches as well as suggest new approaches in research
- Work cross functionally with Product Managers, Senior Leaders in Enterprise Data, Engineering, and other Quant Research teams
You’ll need to have:
- An advanced degree in an applied numerical field: Physics, Mathematics, Statistics, Computer Science, Operations Research, etc.
- Strong quantitative analysis, programming, and statistical modeling skills
- Machine learning experience in a professional role
- Technical skills: Must be proficient in Python and familiar with distributed computing frameworks (e.g. Spark)
- The ability to show special attention to data integrity and robustness of various models, a rigorous scientific/statistical approach and a complete technical background
- Experience in taking on independent research and developing end-to-end modeling solutions to real word problems
- Track record of gathering, matching, and processing large data sets from varied sources and of different characteristics. Analysis on mixed features: continuous and categorical that may be noisy or corrupted
- Solid understanding of different machine learning techniques: dimensionality reduction, representation learning, generative modeling, transfer learning, and missing value imputation, recommender system, causal inference
- Strong communication skills both written and spoken
We’d love to see:
- Financial industry experience
- Reinforcement Learning
If this sounds like you:
Apply if you think we're a good match and we'll get in touch with you to let you know next steps. In the meantime, check out http://www.bloomberg.com/professional.
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