BQuant Enterprise is Bloomberg's brand new, cloud-hosted data science platform designed specifically for financial markets. BQuant Enterprise combines a powerful, Jupyter notebook-based development environment with Bloomberg's comprehensive range of high-quality, market leading, multi-asset-class financial and alternative data sets. Your role:
Machine learning and big data are transforming the world of quantitative investment research. By leveraging distributed computing, firms are able to run experiments using decades of data and tune models against hundreds of parameters.
As a Product Manager for Big Data and ML Toolkits, you'll help Bloomberg bring state of the art computation paradigms (Spark, TensorFlow, etc) to our BQuant Enterprise platform. You'll need to take a multifaceted approach and consider how GPUs, experiment management features, ML toolkits and big data processing engines all work together to enable cutting edge data science.
This is a high-leverage role in a very cross-functional environment so you'll need to be comfortable wearing many hats and balancing technical expertise with business acumen. We'll trust you to:
You'll need to have:
- Interview quants, data scientists and other stakeholders to deeply understand their workflows, synthesize feedback and map user requirements into tangible feature sets
- Own the roadmap for big data and machine learning features within BQuant Enterprise
- Work with a talented engineering team to scope work, solve problems and manage the software delivery pipeline
- Communicate across the organization to align efforts from many departments
- Define key elements of our go-to-market strategy and ensure a smooth product rollout
We'd love to see:
- 5+ years of experience in a Product Management role
- Data science experience using Python, especially Jupyter notebooks and Pandas
- Experience with machine learning and ML toolkits such as TensorFlow or PyTorch
- Experience with Spark and the Hadoop Ecosystem
- Strong organizational and communication skills
- Willingness to roll up your sleeves and help out even when it's not in your job description
- Prior experience in financial services and an understanding of the investment research workflows
- Experience with public cloud providers such as AWS or Azure
- Experience with Kubernetes
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