You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect Data Domain Architect Analyst opportunity for you.

Job summary

The Machine Learning team at JPMorgan Chase combines cutting edge machine learning techniques with the company’s unique data assets to optimize all the business decisions we make. In this role, you will be part of our world-class machine learning team, and work on the collection, annotation and enrichment of data for machine learning models. Our work spans the company’s lines of business, with exceptional opportunities in each.

Job responsibilities

• Work on data labeling tool(s) and annotate data for machine learning models. Shift through structured and unstructured data; identify the right content and annotate with the right label. Collaborate with stakeholders including machine learning engineers, data scientists, data engineers and product managers across all of JPMorgan Chase's lines of businesses, such as Investment Bank, Commercial Bank, and Asset Management.
• Work on engagements from understanding the business objective through the data identification, annotation and validation. Comprehend the subtleties of language used in the financial industry. Conduct research and bring clarity in business definitions and concepts. Annotate the terms, phrases, and data as per the project requirement.
• Understand and define the relationship among entities. Validate model results from the business perspective and provide feedback for model improvement. Effectively communicate data annotation concepts, process and model results to both technical and business audiences. Break down ML annotation topics in a clear manner. Transcribe verbatim audio recordings, single and multi-speaker of varying dialects and accents and identify relevant keywords and sentiment labels
• Build a thorough understanding of data annotation and labeling conventions and develop documentation/guidelines for stakeholders and business partners and develop key workflows, processes and KPIs to measure annotation performance and assess quality.
• Become a subject matter expert and trusted advisor to your business partners to create and structure new annotations, labels and sub-labels. Represent data annotation team on multiple internal forums with other stakeholders.
• Create an effective roadmap and implement best practices of data annotation for production-level machine learning applications.
• Build rapport and work with stakeholders and understand the business use-case. Collaborate with other members in the team to deliver accurate and relevant data annotations. Build data pipelines by implementing data engineering best practices in cloud native applications, data marts and data lakes to ingest unstructured data and to deliver training data seamlessly. Build/create and maintain python scripts for automation of manual efforts to bring efficiency

Required qualifications, capabilities, and skills

• Strong financial knowledge. Full-time masters in a business management (MBA) with finance specialization with 6+ years of applied experience in data collection, analysis or research.
• Should be able to work both individually and collaboratively in teams, in order to achieve project goals.
• Must be curious, hardworking and detail-oriented, and motivated by complex analytical problems and interested in data analytics techniques.
• Interest in Machine learning and should be able to develop a working level domain knowledge on machine learning concepts
• An understanding of model scoring parameters such as precision, recall and f-score
• Intermediate to advance Python skills with hands on in their current role
• Intermediate to advance data engineering (with public cloud) skills with hands in their current role

Preferred qualifications, capabilities, and skills

• Experience in data extraction/collection form financial documents. Experience with data annotation, labeling, entity disambiguation and data enrichment.
• Familiarity with industry standard annotation and labeling methods and Exposure to voice translation services and tools
• Familiarity with Machine learning and AI paradigms such as text classification, entity recognition, information retrieval
• Basic to intermediate skills with Kubernetes, Docker, Containers