Data Domain Architect Analyst

4 weeks ago

Bengaluru, India JPMorgan Chase & Co. Full time

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.

The successful candidate will work on multiple projects and provide data annotation services across a variety of data types including, but not limited to, text, chats, emails and audio. We expect the candidate to understand the business use-case and own the data annotation pipeline to go from the raw data to a reliable, annotated ground truth that can be used by sophisticated machine learning methods for banking applications such as risk assessment, trading models, customer relationship management, and pricing models.

  Responsibilities

Work on data labeling tool(s) and annotate data for machine learning models. Sift 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. Adapt to changes in guidelines, priorities and environments. 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. 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.  Become a subject matter expert and trusted advisor to your business partners to create and structure new annotations, labels and sub-labels.  Collaborate with ML engineering teams to simplify annotation workflows and improve ML model performance. Identify and successfully carry out opportunities to increase productivity with less manual efforts for ML applications through a mixture of ML Algorithms, LLMs, and programmatic scripts.  Enhance labeling workflows and projects success metrics with ways to ensure quality and consistency in annotations. Engage and contribute in strategic programs and initiatives at firm level to identify problems while measuring and improving the quality of annotation deliverables. Identify opportunities to improve tooling, processes and implement scalable solutions Understand end to end data interactions and dependencies across complex data pipelines and data transformation and how they impact business decisions

Qualifications

Strong experience in programming (Python, Unix) Strong academic background [Bachelors/Masters] in machine learning/data science/ data analytics/computer science or other quantitative/science field. Experience in financial industry is a plus.  5+ years of hands-on experience in data science, data analytics or research and leading a team 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.

Beneficial 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 Exposure to voice translation services and tools Familiarity with Machine learning and AI paradigms such as text classification, entity recognition, information retrieval

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