Vice President, Digital Intelligence - Senior Machine Learning Engineer Jpmorgan Chase & Co.
New York, NY

Job Description

Vice President, Digital Intelligence - Senior Machine Learning Engineer

Req #: 190037054

Location: New York, NY, US

Job Category: Digital

Job Description:

About Chase

Chase is the U.S. consumer and commercial banking business of JPMorgan Chase & Co. (NYSE: JPM), a leading global financial services firm with assets of $2.4 trillion and operations worldwide. Chase serves nearly half of America's households with a broad range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. Customers can choose how and where they want to bank: 5,200 branches, 16,000 ATMs, mobile, online and by phone. For more information, go to Chase.com.

Chase Consumer & Community Banking serves nearly 66 million consumers and 4 million small businesses with a broad range of financial services, including personal banking, investment advice, small business lending, mortgages, credit cards, payments and auto financing. In recent years, we have undertaken a large-scale digital transformation initiative, building on the success of our current mobile and online service offerings.

Senior Machine Learning Lead

Description

The 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. Because we are part of Chase Digital, the team is to support and provide advanced solutions to all digital applications wherever we are needed, i.e. we are not limited to a few applications. We work closely with our engineering and technology partners to deploy solutions to reach out customers. We value our customers' direct feedback and function in a truly agile way to incorporate changes to improve application experience.

We are looking for talented candidates who have a strong computer science background, i.e. thinking like a computer scientist where computational optimizations and innovations are applied every day, programming is not an afterthought. Candidates should not be hesitant to take on projects where he/she will everything end-to-end, thinking like an engineer not afraid to get his/her hands dirty and no job is too small or hard, with a core understanding of machine-learning is only a small portion of a project and majority of the projects will be engineering focused.

The candidate needs to have significant training and working experience in computer science, with a few years of hands-on applied machine-learning application development. Big-Data platform work experience is a must, i.e. worked and maintained HBase, Hadoop, Spark, etc. where the candidate has been a key team player. The candidate needs to have significant software engineering work experience, where he/she has been responsible for bringing a complete system to production end-to-end, working with DevOps and various levels of production engineering teams.

Qualifications:

7-10 years of software engineering/developer and production experience

3-5 years of applied machine-learning with regression, classification, etc. models for supervised learning

3-5 years of experience in big-data unsupervised learning

5 years of big-Data platform engineering

5 years of parallel computing

Core foundational training in Computer Science, rooted in computing theories and computational/programming/data optimization. An undergraduate degree in Computer Science is required, with additional graduate degrees also in CS (not pure science background).

Desirable:

5 years of NLP (natural language processing) with demonstrable work products.

Extensive work experience in financial services, with strong understanding of creating the best products for customers in this sector.

Extensive experience in time series analyses.