On the Data Automation team, we develop the machine learning models and infrastructure to automate the processing of all types of financial documents. Our team has built some of the world's most sophisticated deep learning models, which beat the performance of the best analysts in the market. The models we build enable our customers to get the right answers fast.

As part of our team, you will research machine learning solutions and build infrastructure for accurate and scalable solutions. If you're excited by the idea of applying technology and automation to complex data problems, keep reading.

As an engineer in Data Technologies, you’ll be responsible for the systems that onboard all the referential data that drive Bloomberg's applications and enterprise systems. As our clients are shifting more and more to rely on machines to interpret data and drive insights, we are utilizing cutting edge technologies to deliver unparalleled data quality. By joining Data Technologies, you will help us improve the accuracy, coverage, timeliness, and accessibility of our data to service our clients across all of Bloomberg's products. Learn more about the Data Technologies teams at Princeton here: https://www.youtube.com/watch?v=qtUu9LCNmiU

We'll trust you to:

• Build machine learning models to understand documents and drive insights.
• Design and implement efficient pipelines for data manipulation, processing and delivery to our end users
• Create tools for automated quality assurance and anomaly detection to alert stakeholders of changes in the quality of machine learning models and analytics
• Develop quality software through code reviews, automated testing and design reviews

You'll need to have:

• 2+ years of experience programming in Python and C/C++
• A solid understanding of data structures, algorithms and software design concepts
• Experience with machine learning, statistical models and natural language processing
• BA, BS, MS, PhD in Computer Science, Engineering or related technology field

We'd love to see:

• Experience with concurrent programming and distributed systems
• Familiarity with solving problems using heterogeneous hardware
• Exposure to deep learning