Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. 

As a Senior Machine Learning Operations Associate within our Consumer & Community Banking division, you will be responsible for building and maintaining pipelines for model training, batch/real-time model serving, hyperparameter tuning at scale, model monitoring, production validation and other activities vital for model development, testing and deployment in a well-managed, controlled environment.  

Our product, Personalization and Insights, builds and supports high throughput, low latency applications which leverage state of the art machine learning architectures, and which are deployed in AWS.  These applications power personalized experiences across Chase Consumer & Community Banking channels, to help weave a user experience that includes traditional banking services with other services in the Travel, Merchant Offer Shopping, and Dining spaces.

Job responsibilities 

Deploy and maintain infrastructure (eg., Sagemaker Notebooks) for providing an effective model development platform for data scientists and ML engineers that integrates with enterprise data ecosystem   Build, deploy and maintain ingress/egress and feature generation pipelines to calculate input features for model training and inference    Deploy and maintain infrastructure for batch and real-time model serving, in high throughput, low latency applications, at scale.   Identify, deploy and maintain high quality model monitoring and observability tools  Deploy and maintain infrastructure for compute intensive tasks such as hyperparameter tuning and interpretability and explainability  Partners with product, architecture, and other engineering teams to define scalable and performant technical solutions.     Leverages deep technical expertise to design extensible and scalable solutions, and to coach and grow individuals and teams.  Ensures team executes work according to compliance standards, SLAs, and business requirements, to meet the objectives of an initiative.  Anticipates the needs of broader teams and potential dependencies with other teams.    Identifies and mitigates issues to execute a book of work while escalating issues as necessary.  Proactively helps maintain high operational excellence standards for our production systems.  Encourages development of technological methods and techniques

Required qualifications, capabilities, and skills 

BS degree in Computer Science or related Engineering field  3+ years applied experience  Experience in model training, building, deployment and execution ecosystem such as Sagemaker and/or Vertex AI   Experience in monitoring and observability tools to monitor model input/output and features stats  Operational experience in data tools such as Spark, EMR, Ray  Experience and interest in ML model architectures, linear or logistic regression, Gradient Boosted Trees, Neural Network architectures  Experience in containers like docker ecosystem, container orchestration systems like Kubernetes, ECS and Airflow, Kubeflow etc. Experience with cloud technologies such as EC2, Sagemaker, IAM 

 

Preferred qualifications, capabilities, and skills

Bias for action and iterative development  Experience with recommendation and personalization systems is a plus.  Experience in programming languages such as Python and Java  Familiarity of Databases