FA - Machine Learning Research Engineer - Fixed Income Pricing
Bloomberg absorbs billions of data points from hundreds of financial markets every day. Our developers build applications that fuel the markets by providing intelligent analytics and transparency into these markets. Our customers rely on us to understand markets and analyze complex structures which allow them to make smart investment decisions.
Our enterprise product BVAL is the gold standard for pricing. It generates end-of-day prices for about 3 million fixed income securities and curves. Our suite of applications also includes high-precision pricing algorithms, liquidity hubs for storing and retrieving pricing information, and big data analytics. As the Fixed Income markets feel the need for more continuous pricing, we are working on making our systems more distributed, highly available and continuous. We like to roll up our sleeves, collaborate seamlessly and deliver solutions to clients across all industries.
As a member of this team, you'll be joining during exciting times as we research new prediction models for real-time pricing of millions of bonds. We'll trust you to
You'll need to have
- Develop bespoke ML/AI prediction models for financial times series data
- Write and maintain production-quality code
- Collaborate with ML engineers on model maintenance and rollout
We'd love to see
- Experience with modern deep learning frameworks such as PyTorch or TensorFlow
- Experience in at least one modern programming language such as Python, C++, Java.
- BA, BS, MS, PhD in Computer Science, Electrical Engineering or related technology field
- Prior publications in AI or Machine learning.
- Experience with model validation, backtesting in a noisy data environment
- Interest in learning more about the Fixed Income market (prior experience with finance is not required).
Bloomberg is an equal opportunities employer, and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.