Overview
HybridNew Jersey, Atlanta, Chicago, Ohio
Depends on Experience
Contract - W2
Contract - 12 Month(s)
Skills
Python
Gen AI
Job Details
Role: Data Analyst
Location: Chicago/Atlanta/NYC/NJ/ Cleveland, OH (Hybrid 3 days a week must)
Location: Chicago/Atlanta/NYC/NJ/ Cleveland, OH (Hybrid 3 days a week must)
Long Term
Primary skills: Python, Gen AI
Good to have Insurance domain- P&C
Experience: 10 years
Who has experience preparing datasets for LLM-based applications, Retrieval-Augmented Generation (RAG) or chatbot systems.
Who is comfortable working with structured and semi-structured insurance data to build curated knowledge inputs.
Who can annotate fields, data structures, documents, create prompt-response structures, and support semantic chunking strategies.
Who can contribute to the development of our semantic model, including defining taxonomies, ontologies, and synonym sets for insurance concepts.
Who is proficient in SQL (Snowflake preferred) and able to extract and transform content from large data systems.
Who understands how LLMs retrieve and process contextual information, including embedding logic and metadata tagging.
Who is familiar with data governance best practices, especially in regulated domains like insurance, banking, or finance.
Who has completed a quantitative course such as Mathematics, Statistics, or Computer Science/Engineering. (Preferred, not necessary)
Soft Skills:
Analytical Thinking: Strong ability to identify patterns, gaps, and inconsistencies in large data sets.
Research Skills: Able to explore domain-specific terminology and translate it into structured knowledge.
Collaboration: Comfortable working closely with engineering, data, and domain SMEs in a distributed team.
Primary skills: Python, Gen AI
Good to have Insurance domain- P&C
Experience: 10 years
Who has experience preparing datasets for LLM-based applications, Retrieval-Augmented Generation (RAG) or chatbot systems.
Who is comfortable working with structured and semi-structured insurance data to build curated knowledge inputs.
Who can annotate fields, data structures, documents, create prompt-response structures, and support semantic chunking strategies.
Who can contribute to the development of our semantic model, including defining taxonomies, ontologies, and synonym sets for insurance concepts.
Who is proficient in SQL (Snowflake preferred) and able to extract and transform content from large data systems.
Who understands how LLMs retrieve and process contextual information, including embedding logic and metadata tagging.
Who is familiar with data governance best practices, especially in regulated domains like insurance, banking, or finance.
Who has completed a quantitative course such as Mathematics, Statistics, or Computer Science/Engineering. (Preferred, not necessary)
Soft Skills:
Analytical Thinking: Strong ability to identify patterns, gaps, and inconsistencies in large data sets.
Research Skills: Able to explore domain-specific terminology and translate it into structured knowledge.
Collaboration: Comfortable working closely with engineering, data, and domain SMEs in a distributed team.
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