Enterprise Data Quant Researcher to will apply cutting edge machine learning techniques to financial modeling problems by leveraging the large and varied datasets within Bloomberg Enterprise Data.
- 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
- Assesses quality controls around different approaches as well as suggesting new approaches in research
- Work cross functionally with Product Managers, Senior Leaders in Enterprise Data, Engineering, and other Quant Research teams
- Advanced degree in an applied numerical field: Physics, Mathematics, Statistics, Computer Science, Operations Research, etc.
- Strong quantitative analysis, programming, and statistical modeling skills
- 2+ years of machine learning experience in a professional role
- Technical skills: Must be proficient in Python and familiar with distributed computing frameworks (e.g., Spark). Scala is a plus, but not required
- 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
- Strong communication skills both written and spoken
About the company
Bloomberg is the world's primary distributor of financial data and a top news provider of the 21st century. A global information and technology company, we use our dynamic network of data, ideas and analysis to solve difficult problems every day. Our customers around the world rely on us to deliver accurate, real-time business and market information that helps them make important financial decisions.