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

JPMorgan Chase & Co.; New York (NY); On-site; USD 90,000 - 160,000; Full time

Job summary

An established industry player is seeking a talented individual to join their Chief Data & Analytics Office. This role involves tackling complex challenges through innovative machine learning and artificial intelligence solutions. You will have the opportunity to develop cutting-edge models that enhance productivity and risk management while collaborating with various teams across the organization. If you are passionate about data-driven decision-making and eager to contribute to transformative projects, this position offers a unique chance to make a significant impact in the financial services sector.

Qualifications

• PhD or MS in quantitative fields with relevant experience.
• Solid background in machine learning, NLP, and analytics.

Responsibilities

• Research new machine learning methods and develop state-of-the-art models.
• Collaborate with teams to deploy solutions and drive firm-wide initiatives.

Skills

Machine Learning

Natural Language Processing (NLP)

Deep Learning

Statistical Analysis

Data Science

Experiment Design

Analytical Problem Solving

Education

PhD in a quantitative discipline

MS with industry or research experience

Tools

TensorFlow

PyTorch

NumPy

Scikit-Learn

Pandas

Job description

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.

Job 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 and apply it 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.

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 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 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.
• 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 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.