Senior Operations Analyst

Required Qualifications

• Bachelor’s degree in Finance, Business, Accounting, or related field
• 7–10 years of experience in:
  • Insurance claims processing / finance operations
  • Data validation / QA / document review roles
• Strong attention to detail and analytical skills
• Experience in reviewing and validating large volumes of data
• Proficiency in MS tools with the ability to analyze details in Excel Sheets
• Experience in SOP creation and process improvement initiatives
• Strong written and verbal communication skills

Preferred Qualifications

• Experience in AI data annotation / HITL workflows
• Familiarity with Intelligent Document Processing (IDP) tools

Key Responsibilities

Data Validation & Review

• Validate AI-extracted data from insurance claims, invoices, contracts, and financial documents
• Compare system outputs with source documents and identify discrepancies
• Correct errors and ensure completeness and accuracy of data
• Handle structured and unstructured data across multiple document types

Quality Assurance

• Perform QA checks on AI outputs and annotated datasets
• Ensure adherence to data standards and review guidelines
• Maintain high consistency in data validation processes

Domain-Specific Validation

• Review insurance and finance-related data such as:
  • Claims details, claim amounts, policy data
  • Premiums, deductibles, billing, and invoice data
  • Financial figures and document fields
• Ensure compliance with business rules and regulatory standards

Process Documentation & Improvement

• Follow and contribute to Standard Operating Procedures (SOPs)
• Document workflows, requirements, and process updates
• Conduct root cause analysis (RCA) to identify recurring issues
• Suggest improvements to enhance process efficiency and AI accuracy

Collaboration & Escalation

• Escalate complex or ambiguous cases to project leads/SMEs
• Provide structured feedback to AI/ML teams on model errors
• Support training and mentoring of junior reviewers

Operational Excellence

• Meet defined accuracy, productivity, and turnaround time (TAT) targets
• Maintain quality logs, audit trails, and performance metrics
• Ensure compliance with data privacy and confidentiality policies