JPMorgan Machine Learning Scientist - Speech AI NLP Sr Associate 

United States, New York, New York 

746014633

Job Responsibilities

• Research, develop and productionize high performance machine learning models, quantitative models, and applications.
• Collaborate with all of JPMorgan's lines of business and functions to deliver software solutions, making high 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

• PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science with three years of industry experience Or an MS with at least five years of industry or research experience in the field
• Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods
• Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch)
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
• Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
• Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
• Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problems

Preferred qualifications, capabilities, and skills

• Strong background in Mathematics and Statistics
• Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
• Knowledge in Deep Learning frameworks
• Cloud computing: Google Cloud, Amazon Web Service, Azure, Docker, Kubernetes.
• Experience in distributed system design and development