Nimble Gravity
commercial property insurance
AIDataQualityAnalyst(Human-in-the-Loop)
Neural analysis suggests this role is
optimal for Mid candidates.
“AI Data Quality Analyst (Human-in-the-Loop) at Nimble Gravity. Skills: Data Quality, AI, Document Analysis, Requirements Engineering. Review AI-extracted data. Compare extracted fields against source documents”
What You'll Achieve.
ensuring that downstream tools receive clean, reliable data; improving our AI systems; client’s underwriting workflow; output meets agreed business and underwriting expectations; defining metrics and dashboards for data quality and model performance
Industry & Context.
Excellent analytical skills; trace issues from symptoms back to likely root causes; Curiosity; pattern recognition; Structured thinking
Work primarily in Eastern European time zones
What They're Looking For.
Must Have
3+ years of experience in a data-intensive role, data analyst, business analyst, QA analyst, operations analyst, attention to detail, systematic, repetitive data review, Excellent analytical skills, write clear, structured tickets/requirements, Advanced Excel skills, pivot tables, lookups, filters, data cleansing techniques, working with complex business documents, written English, reviewing many documents/records per day, issue-tracking or project management tools, Ability to work independently, manage your own queue, escalate appropriately
Nice to Have
Experience in commercial insurance, property insurance, financial services operations, Statements of Values (SOVs), loss runs, risk/coverage documentation, Exposure to LLMs and AI systems, document intelligence, RAG, chat agents, Basic familiarity with SQL, query tools, Prior experience in a Human-in-the-Loop (HIL) role, data quality role supporting machine learning models, Experience defining data quality metrics, working with data quality metrics, accuracy, completeness, precision/recall
What You'll Do.
Review AI-extracted data
Compare extracted fields against source documents
Identify discrepancies
Identify patterns and root causes
Translate observed patterns into requirements
Collaborate closely with architects
refine extraction rules
Use LLMs and AI assistants
Help continuously improve documentation
Contribute to defining metrics
dashboards for data quality
How You'll Work.
Team & Collaboration
Collaborate closely with architects, data engineers, and other analysts; communicating with distributed teams; working with engineers, architects, and business stakeholders across time zones
Communication Scope
written English; Communication; collaboration
Process & Methodology
manage your own queue, escalate appropriately
Full Job Description
AI Data Quality Analyst (Human-in-the-Loop) About the Role We are looking for a hands-on AI Data Quality Analyst (Human-in-the-Loop) to support a strategic client in the commercial property insurance space. This role sits at the intersection of data quality, QA, and product thinking. You will be the “human in the loop” for an AI-powered document processing pipeline: reviewing what the AI extracts from complex insurance submissions (e.g., Statements of Values (SOVs), loss runs, spreadsheets, PDFs), correcting errors, and ensuring that downstream tools receive clean, reliable data. On top of the day-to-day “grind work” of validation and correction, you’ll zoom out to identify recurring issues, spot patterns, and translate them into clear requirements and bug reports for the engineering team. This is not a Product Manager role and not a “purely strategic” position. It is very hands-on, detail-oriented work that is critical to improving our AI systems and ultimately the client’s underwriting workflow. What You’ll Do Review AI-extracted data from insurance submissions (SOVs, loss runs, supporting documents) for accuracy, completeness, and consistency. Compare extracted fields against source documents, identify discrepancies, and correct data directly in the appropriate systems or templates. Act as a quality gate for the AI pipeline, ensuring output meets agreed business and underwriting expectations before it moves downstream. Log issues, defects, and edge cases with clear reproduction steps, examples, and impact, using tools like Jira or similar. Identify patterns and root causes behind extraction errors (e.g., recurring issues with specific formats, document types, or fields). Translate observed patterns into well-structured requirements, user stories, and bug reports that engineering and data teams can act on. Collaborate closely with architects, data engineers, and other analysts to refine extraction rules, templates, and workflows. Use LLMs and AI assistants as tool
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