What we do:
Bloomberg's primary product is our data. Our Global Data department sits at the heart of our enterprise and combines technology with deep product expertise to bring unequalled value to the world's information. We bring structure to the data we collect by building robust systems with innovative technology that allow us to offer unique data products that drive our client's biggest financial decisions.
Our Data Engineers and ML Automation Developers are at the forefront of this process; we create cutting-edge systems to manage the relentless flow of information and support hundreds of data analysts, data scientists and product experts around the world in bringing transparency and meaning to data that makes Bloomberg the world's premier provider of information.
This is not your typical Data Engineering position.
Our department is a dynamic, creative team with a focus on up-ending the status quo and leveraging our technology and resources in new ways. You'll feel more like you're in a well-funded startup than in a global enterprise with over 170 locations, and we'll expect a positive, proactive attitude to match.
Who you are:
You thrive in environments where the data is unstructured and have worked with data sources where it's a challenge to separate the signal from the noise. You're up to date with the latest developments in the Big Data ecosystem, are experienced with industry-standard ML techniques and know how to deliver ML systems to production. You'll combine Bloomberg's state-of-the art technology resources with your existing industry AI and ML knowledge to identify opportunities for new applications of ML to improve and create products. As a leader, you're able to explain complex concepts to non-technical stakeholders and can cut through the technical jargon to focus on key outcomes.
What you'll work on:
You'll be part of a team building data-driven systems supporting our internal businesses across Data Analytics, News, Research & Enterprise Data. Typical responsibilities include:
* Design ML algorithms to deploy at the core of ETL and stream processing pipelines in our serverless microservice infrastructure built on top of industry standard technology like Kubernetes & Kafka.
* Leverage your knowledge of industry standard ML algorithms to solve problems of anomaly detection, clustering and time series prediction on web-sourced and financial content.
* Lead architecture and design decisions on how to build scalable ML data pipelines and integrate our systems with the broader Bloomberg infrastructure. Experiment with internal and external ML infrastructure to keep our stack up to date.
* Build annotation workflows using open source and our advanced in-house systems to gather expert user labelled data for your models. Explore methods of gamification and other strategies for improving data quality.
* Implement robust monitoring and evaluation of algorithms and processes you are responsible for - leveraging splunk, humio, grafana and web-apps. You put the evaluation metrics in the hands of your stakeholders.
What you need to have (give or take a few):
* A BA/BS degree or higher in Computer Science, Mathematics, or relevant data technology field, and 2-5 years of professional work experience in software development, ML Data Engineering, data science or a related field.
* Standard dev skills: Linux, virtualized/cloud environment, git, scripting, automated testing.
* An awesome attitude and a collaborative working style.
* Strong verbal and written communication skills, especially when explaining technical solutions to stakeholders and management. Experience with data visualization and presentation tools; you can tell a compelling story about the data you work with.
Does this sound like you? Does this almost sound like you? Hit apply and we'll be in touch! People you'll work with:
We are an equal opportunity employer and 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.