# Voice AI QA Analyst at Veritus > Remote · United States · Unspecified · QA / Test · open. Source: https://alderhq.app/jobs/ashby%3Averitus%3Ad891044d-2a3e-4d9c-9106-4ea963fbf3b5 (read 2026-11-15). ## Facts (observed) - Company: [Veritus](https://alderhq.app/companies/veritus) - Location text: San Francisco - Where it can be done from (inferred): Remote · United States (evidence: "Location: San Francisco") - Posted: 2026-10-04 (42 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-10 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/veritus/d891044d-2a3e-4d9c-9106-4ea963fbf3b5 - Skills listed (inferred): SQL ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 100): p25 $135k, median $154k, p75 $200k ## Description (the employer's text) ### Voice AI QA Analyst Veritus Agent builds AI agents for borrower communication across the consumer lending lifecycle: origination, servicing, and collections. Every agent we deliver is tested against real conversations before it reaches a client, and every live agent is continuously monitored for quality and performance. This role sits at the intersection of testing, QA, and performance analytics. You’ll help ensure our agents are ready for launch, monitor how they perform in production, and use both individual call reviews and aggregate call data to identify opportunities to improve performance for our clients. ### Responsibilities Conduct structured UAT on AI voice agents prior to client launch: place and receive test calls, probe edge cases and failure modes, and document defects with clear, reproducible steps Test across scripted and adversarial scenarios (identity verification, payment negotiation, escalation/handoff triggers) to confirm the agent behaves as expected before go-live Monitor the ongoing performance of live agents across client deployments, using both call-level QA and aggregate performance metrics Listen to and grade live agent calls against Veritus's QA scorecard, scoring for accuracy, compliance, and tone Analyze call data to identify trends, recurring failure modes, outliers, and opportunities to improve agent performance Track performance metrics over time and investigate changes or anomalies in agent behavior Partner with Customer Success to prepare quarterly client performance reports, combining quantitative analysis with insights from call reviews Help translate call and performance data into clear, actionable recommendations for clients and internal teams Flag systemic issues—including recurring failure patterns, scorecard drift, and emerging edge cases—to customer success and engineering teams Maintain clear, consistent documentation of test results, call grades, analyses, and findings in our tracking tools ### Minimum Qualifications 2–4 years of QA, quality analyst, operations analyst, or similar experience, ideally in a consumer lending contact center or voice-based environment Familiarity with US consumer lending laws and regulations such as FDCPA, TCPA, ECOA, and SCRA Strong attention to detail and comfort following structured test plans and scorecards Comfortable working with quantitative data and basic statistics to identify trends and assess performance Experience working with spreadsheets or data analysis tools to analyze operational or customer interaction data Ability to turn quantitative and qualitative findings into clear conclusions and recommendations Clear written English for defect reports, call notes, and client-facing performance summaries Comfortable giving and receiving detailed feedback on quality issues ### Nice-to-have (but not required) Experience testing AI/voicebot or IVR systems Prior UAT or pre-release testing experience for a software product Experience analyzing contact-center or call-performance data Experience creating client-facing performance reports or working closely with Customer Success teams Familiarity with SQL, BI tools, or other data analysis tools ### Education Requirements BS preferred; equivalent professional QA, operations, or analytics experience is sufficient --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Averitus%3Ad891044d-2a3e-4d9c-9106-4ea963fbf3b5 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.