Machine Learning Data Domain Architect Lead

JPMorgan Chase - Wilmington, DE

Full-time - Mid Level

Wilmington, DE

Credit Intermediation and Related Activities

About the position

The Machine Learning Data Domain Architect Lead plays a pivotal role in enhancing Consumer and Community Banking Operations through the application of machine learning and artificial intelligence. This position focuses on leveraging high-quality annotated data to develop personalized solutions for customers, ensuring that data is effectively collected, curated, and validated to support impactful AI/ML algorithms. The role involves leading a team of analysts and collaborating with various stakeholders to optimize training data and improve machine learning outcomes.

Responsibilities

• Manage and coach a team of Machine Learning Data Domain analysts to support data annotation and label data/content using annotation tools and analysis.
• Partner with leads in Data Science, Engineering, and Analytics to develop strategies to optimize training data for machine learning models.
• Lead efforts to identify patterns and trends in conversational data through Natural Language Processing and/or other computational linguistic approaches.
• Collaborate with stakeholders on evaluating the quality of machine learning classification and other output.
• Actively contribute to the team's continuous learning mindset by bringing in new ideas and perspectives that stretch the thinking of the group.

Requirements

• Generally 6+ years of related experience.
• Excellent analytical and problem-solving skills and the ability to pay close attention to detail.
• Experience using Python in working with and analyzing large real-world datasets.
• Working knowledge of information and data retrieval.
• Working knowledge of machine learning and artificial intelligence paradigms and libraries.
• Technical understanding of common relational database systems; i.e., Teradata and Oracle.
• Advanced understanding of Hadoop-related technologies & their applications.
• Hands-on experience with various data modeling techniques and tools.
• Excellent command of the SQL language.
• Knowledge of SAS or Scala, and Python languages.
• Knowledge of Advanced Statistics.