What are the responsibilities and job description for the Data Domain Architect Lead position at JPMorgan Chase?
Machine Learning and Artificial Intelligence play a critical role in transforming Consumer and Community Banking Operations. The ability to utilize data in meaningful ways allows us to develop solutions which both our customers and employees can benefit from. Customers expect tailored servicing and Chase is looking to deliver personalization to meet their needs. This is powered by high-quality annotated data and detailed annotation schemes that are the backbone of impactful Artificial Intelligence/Machine Learning ( AI/ML)L algorithms and applications.
As a Data Domain Architect Lead within the Data Annotation team , you will use your domain expertise and people-leading experience to partner your team closely with teams in Data Science, Analytics, and Engineering to develop machine learning solutions. This will involve the collection, curation, annotation, enrichment, and validation of data and the development of taxonomies and other linguistic resources to help train machine learning models, drive insight, analysis, and possible content creation.
Job 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
Required qualifications, capabilities, and skills
- 6 years of related experience in development of machine learning solutions
- Familiar with industry annotation and labeling methods
- Experience with various data modeling techniques and tools
- Familiar with Finance and Banking products
- Broad expertise in data technologies; i.e., data warehousing, data processing, data quality concepts, Business Intelligence tools and analytical tools, unstructured data, machine learning
- 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
- Familiar with Large Language Models (LLMs) and prompt engineering
Preferred qualifications, capabilities, and skills
- Masters or PhD in a related field, or Bachelors
- Technical understanding of common relational database systems; i.e., Teradata and Oracle
- Excellent command of the Structured Query Language (SQL)
- Knowledge of SAS or Scala, and Python languages
- Knowledge of Advanced Statistics
- Advanced analytical thinking and problem-solving skills
- Strong interpersonal & communication skills