DescriptionThe Digital Intelligence team’s mission is to utilize large-scale computation, true large-scale data set, and apply machine-learning to our most critical and wide-range customer products. The number of products and practice areas is large and far-reaching, i.e. we work on products that are impactful to our millions and millions of customers and households. We value our customers’ direct feedback and function in a truly agile way to incorporate changes to improve application experience.
As an applied Machine Learning engineer in Consumer & community banking, you will be working on exciting end-end machine learning problems and solutions. You will be working with the team to solve large scale relevance and ranking problems, engineer scalable features and is comfortable doing ML Ops work to build brand new systems that benefit Chase customers across all lines of business.
Job Responsibilities:
- Lead complex projects that require cross-organizational alignment and collaboration
- Analyze complex datasets used to make decisions regarding real-world applications
- Investigate the applicability of new approaches to business problems and products by combining theory and experimentation
- Write software code that is built and deployed in production systems
- Anticipate and consider risks when building machine learning solutions and prediction/classification systems
- Communicate complex issues clearly and credibly to consult on and approve team decisions while driving broader organizational actions.
- Work transparently cross-functionally and influence your peers to do the same.
Required qualifications:
- Master’s degree with 7 + years of experience of or Ph.D. in Computer Science or Machine Learning related degree with good internships and about 2 + years of work experience
- Expert in one or more of the following: machine learning, Graph learning, recommendation systems, network analysis, Natural language processing, Reinforcement learning, MLOps
- Comfortable conducting design and code reviews
- Experience with leadership of experienced scientists as well as a record of developing junior members from academia/industry to a career track in a business environment
Preferred qualifications:
- A strong technical advocate with a background in Java or Scala, and Python.
- Comfortable working in a cloud environment like AWS/GCP or Azure
- Passionate about working with large unstructured and structured datasets