The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.

This role offers the unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates. 

As a Machine Learning Scientist - Natural Language Processing (NLP) - Senior Associate within our innovative team, you will apply advanced machine learning techniques to tackle complex challenges such as natural language processing, speech analytics, time series analysis, reinforcement learning, and recommendation systems. You will collaborate with diverse teams and contribute to our knowledge-sharing community. We seek team members who thrive in a collaborative environment, working closely with business partners, technologists, and control teams to implement solutions in production. If you are passionate about machine learning and enjoy dedicating time to learning, researching, and experimenting with new advancements in the field, this role is ideal for you. We highly value expertise in Deep Learning, hands-on implementation experience, strong analytical skills, a deep desire to learn, and high motivation.

Job responsibilities

Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing communityDevelop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systemsCollaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into productionDrive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
 

Required qualifications, capabilities, and skills

PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science Or an MS with at least 3 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 methodsExtensive experience with machine learning and deep learning toolkits  (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goalsExperience 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 environmentsSolid 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 and familiarity with the financial services industries and continuous integration models and unit test developmentKnowledge in search/ranking, Reinforcement Learning or Meta LearningExperience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality codePublished research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal