From the biggest banks to the most elite hedge funds, financial institutions need timely, accurate data to capture opportunities and evaluate risk in fast-moving markets. For over 30 years, our clients have relied on our core product, the Bloomberg Terminal, to access the data and analytics they need to make informed investment decisions.
The Derivatives Data Team is building an all-inclusive solution to bring transparency, efficiency, and excellence to this complex market. To produce real time and accurate data analytics for our clients, we are building a data pipeline using Kafka Streaming, Spark, and Cassandra with data quality evaluations utilizing machine learning. We are looking for engineers to help explore low latency scoring of streaming data, online machine learning, and real time missing data point prediction.
As part of this innovative team, you will be building a groundbreaking market data platform which will revolutionize the data generation for derivatives market. Our system has to be ultralow latency to handle the traffic of millions of market movements per second. We also have complex analytics like source confidence evaluation (scoring), and the need of real-time online learning for prediction. And of course, like any large scale system, it needs to be scalable enough to handle millions of topic subscriptions. You will have the opportunity to work with a multitude of technologies: Spark, Kafka, Flink.
You will work at the intersection of technology, data science, finance, and mathematics. The goal of this platform is to analyze, predict, clean, and generate the best quality derivatives data.