About
J.P. Morgan is a global financial services leader that provides strategic advice and products to corporations, governments, wealthy individuals, and institutional investors. Its Commercial & Investment Bank operates across banking, markets, securities services, and payments, providing strategic advice, raising capital, managing risk, and extending liquidity in markets around the world.
Responsibilities
•Build scalable Data Science capabilities for multiple business use cases
•Collaborate with software engineers to design and deploy Machine Learning services integrated with strategic systems
•Research and analyse datasets using statistical and machine learning techniques
•Communicate AI capabilities and results to technical and non-technical audiences
•Document approaches, techniques, and processes to comply with industry regulation
•Collaborate with cloud and SRE teams and take a leading role in designing and delivering production architectures
•Act as an individual contributor; optional management responsibility may be available depending on experience
Requirements
•Master's or PhD in a quantitative discipline, such as Computer Science, Mathematics, or Statistics
•Solid understanding of statistics, optimization, and ML theory, with familiarity with deep learning architectures such as transformers, CNNs, and autoencoders
•Specialism or well-researched interest in NLP
•Broad knowledge of MLOps tooling for versioning, reproducibility, and observability
•Experience monitoring, maintaining, and enhancing existing models over an extended period
•Extensive experience with PyTorch and related data science Python libraries such as pandas
•Experience containerising applications or models for deployment using Docker
•Experience with a major public cloud provider: Azure, AWS, or GCP
•Ability to communicate technical information and ideas clearly at all levels and build trust with stakeholders
Nice to have
•Designing or implementing DAG-based pipelines using Kubeflow, DVC, or Ray
•Big data technologies
•Constructing batch and streaming microservices exposed as REST/gRPC endpoints
•Container orchestration tools such as Kubernetes or Helm
•Open-source NLP datasets and benchmarks
•Implementing distributed, multi-threaded, or scalable applications
•A track record of developing and deploying business-critical machine learning models
