- New York, NY, USA
- Permanent, Full time
- Credit Suisse -
- 24 Sep 18
Data Strategy/Machine Learning BA
The Equities Technology team at Credit Suisse builds and operates one of the largest electronic trading platforms in the world. In this role, you will be part of our world-class IT team and advance the state-of-the-art in trading applications ranging from pricing to order management to algorithmic trading to market connectivity. Our work spans all product lines in the Equities space, each with its own unique challenges and opportunities.
As a kick-off assignment, the successful candidate will perform analyses of the existing transaction data models and develop a single, comprehensive data model that encompasses all product flows. This model must be supportive of traditional query approaches as well as advanced machine-learning techniques. You will work broadly across the Equities lines of business to actively identify important data problems and incorporate solutions into the canonical model. The ultimate goal of this assignment will be to build a central, cloud-based repository of all Equities trading data that is capable of supporting a variety of needs from streaming analytics to algo backtesting to monitoring to regulatory reporting to archival. Additional responsibilities will include presenting solution proposals, securing approval from stakeholders and working with the solution architect to ensure business objectives are met in all solution designs.
- 8+ years of experience in business analysis and complex data modeling
- Familiarity with financial services products and services, ideally in the Equities space
- Familiarity with modern cloud-based data processing tools such as Hadoop, Spark, Hive, etc
- Familiarity with basic data table operations (SQL, Hive, etc.)
- Familiarity with one or more machine learning APIs and computational packages (e.g. TensorFlow) is a plus
- Hands-on experience with a major public cloud provider such as AWS or Azure is a plus
- Must be able to effectively communicate technical concepts and results, verbally and in writing, to both technical and business audiences