Machine Learning Scientist - NLP - Associate - Machine Learning Center of Excellence

JPMorgan Chase - New York, NY

Full-time - Entry Level

New York, NY

Credit Intermediation and Related Activities

About the position

The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is at the forefront of the firm's data and analytics journey, focusing on ensuring the quality, integrity, and security of the company's data. This role is pivotal in leveraging data to generate insights and drive decision-making, while also developing and implementing solutions that align with the firm's commercial goals. The CDAO harnesses artificial intelligence and machine learning technologies to innovate new products, enhance productivity, and improve risk management in a responsible manner. As a Machine Learning Scientist specializing in Natural Language Processing (NLP) at the Associate level, you will engage with complex challenges that have the potential to transform banking operations. This position allows you to apply advanced machine learning techniques to various tasks, including NLP, speech analytics, time series analysis, reinforcement learning, and recommendation systems. You will work collaboratively with diverse teams and contribute to a knowledge-sharing community, emphasizing a strong collaborative spirit with business partners, technologists, and control partners to effectively deploy solutions into production. We are seeking candidates who are passionate about machine learning and are committed to continuous learning, research, and experimentation in the field. A solid foundation in Deep Learning, hands-on implementation experience, strong analytical skills, and a high motivation to learn are essential for success in this role.

Responsibilities

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

Requirements

• PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science) or an MS with at least 1 year of industry or research experience in the field.
• Solid background in NLP or speech recognition and analytics, personalization/recommendation, and hands-on experience with machine learning and deep learning methods.
• Extensive 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 goals.
• Experience with big data and scalable model training, along with solid written and spoken communication skills to effectively convey technical concepts and results to both technical and business audiences.
• Scientific thinking with the ability to invent and work both independently and in highly collaborative team environments.

Nice-to-haves

• Strong background in Mathematics and Statistics, familiarity with the financial services industry, and continuous integration models and unit test development.
• Knowledge in search/ranking, Reinforcement Learning, or Meta Learning.
• Experience 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 code.
• Published research in areas of Machine Learning, Deep Learning, or Reinforcement Learning at a major conference or journal.

Benefits

• Comprehensive health care coverage
• On-site health and wellness centers
• Retirement savings plan
• Backup childcare
• Tuition reimbursement
• Mental health support
• Financial coaching