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 Machine Learning Quant Researcher will develop new machine learning methodologies and new state of art models that are applicable to real world financial problems. In this role you will:
You'll need to have:
- Be responsible for conducting statistical analysis, developing machine learning methodologies, model estimation and overseeing part of the research activities
- 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
- Write high quality machine learning research papers potentially published on top machine learning conferences/journals
- Work cross functionally with Product Managers, Senior Leaders in Enterprise Data, Engineering, and other Quant Research teams
We'd love to see:
- An advanced degree in an applied numerical field: Physics, Mathematics, Statistics, Computer Science, Operations Research, etc.
- Strong quantitative analysis, programming, and statistical modeling skills
- 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 writing research papers
- 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
If this sounds like you:
- Financial industry experience
- Publications on top machine learning conferences/journals
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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