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 architect, design and engineer a distributed machine learning pipeline for scalable backtesting of pricing models and real-time pricing of millions of bonds.

We’ll trust you to

• Design and build efficient ETL pipelines to accelerate model deployment cycle
• Help deploy prediction models at scale
• Collaborate with quants and application engineers on model rollout
• Build model validation, back-testing pipelines, and metrics dashboards

You’ll need to have

• 3+ years of hands-on experience in Python/C/C++ development
• Prior experience in end to end deployment of ML models and systems
• Experience with a NoSQL database and with messaging systems such as Kafka
• BA, BS, MS, PhD in Computer Science, Electrical Engineering or related technology field

We'd love to see

• Experience with modern deep learning frameworks such as Tensorflow, PyTorch
• Experience with distributed computing platforms such as Spark

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.