JPMorgan Chase is undergoing a major transformation to become a technology-driven, client-centric leader in financial services. With over $2 trillion in assets and operations in more than 60 countries, the firm is investing heavily in next-generation infrastructure, data, and AI technology, supported by an annual tech budget exceeding $10 billion. The Digital Intelligence team is at the forefront of this transformation, leveraging large-scale computation and machine learning to enhance a wide range of critical customer products. Working in close partnership with engineering and technology teams, the group deploys impactful solutions to millions of customers, continuously improving applications through agile development and direct customer feedback.

As a Applied AI ML Director at JPMorganChase within the Consumer & Community Banking AI engineering team, you will have the opportunity to collaborate across all lines of business, designing and implementing scalable data processing pipelines and developing high-quality machine learning models and platforms.
 

Job Responsibilities:

Collaborate with all of JPMorgan’s lines of business and functions to delivery software solutions.

Experiment, develop and productionize high quality machine learning models, services and platforms to make huge technology and business impact.

Design and implement highly scalable and reliable data processing pipelines and perform analysis and insights to drive and optimize business result.

 Required qualifications, capabilities and skills: 

MS with 10+ years of experience or PhD with 7+ years of experience and a degree in Computer Science, Statistics, Mathematics or Machine learning related field.

Solid programming skills with Java, Python or other equivalent languages.

Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics.

Expert in at least one of the following areas: Natural Language Processing, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.

Knowledge of machine learning frameworks: Tensorflow, Caffe/Caffe2, Pytorch, Keras, MXNet, Scikit-Learn.

Experience in ETL data pipelines both batch and real-time data processing, Data warehousing, NoSQL DB.

Strong analytical and critical thinking skills.

Self-motivation, great communication skills and team player.

Preferred qualifications, capabilities and skills :

Cloud computing: Google Cloud, Amazon Web Service, Azure, Docker, Kubernetes.

Experience in big data technologies: Hadoop, Hive, Spark, Kafka.

Experience in distributed system design and development