Voice of Customer (VOC) Insight Analyst

Plaud is building the real-world AI interface for professionals to amplify intelligence, elevate productivity and performance, loved by over 2,500,000 users worldwide since 2023. With a mission to amplify human intelligence, Plaud captures, structures, and compounds the intelligence generated in conversations — so humans can think better, decide faster, and execute with clarity.

Plaud Inc. is a Delaware-incorporated, San Francisco-based company pushing the boundary of human–AI intelligence through a hardware–software combination. With full ISO 27001, ISO 27701, SOC 2, GDPR, EN18031, and HIPAA compliances, Plaud is committed to the highest standards of data security and privacy protection. To learn more about Plaud, please visit https://www.plaud.ai and follow along on Instagram, X, Facebook, LinkedIn, and YouTube.

Plaud is building the next generation intelligence infrastructure and interfaces to capture, extract, and utilize intelligence from what people say, hear, see, and think.

• Plaud is a bootstrapped, skyrocketing, profitable company with a $300M revenue run rate achieved in just three years.

• Define the next-gen paradigm for human-AI interaction.

• Gain exposure to cutting-edge AI for Pro tools and play a direct role in our global expansion.

• Work with passionate teammates who value innovation, collaboration, and customer success.

• Grow your career in a culture that champions continuous learning and fast career development.

• Market-competitive compensation, global exposure, and a vibrant, creativity-fueled work atmosphere.

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  Own the VoC taxonomy and data-quality standard: define issue categories, tagging rules, and quality checks for priority feedback sources (support tickets, reviews, surveys, behavioral and business data); partner with Data Analytics / Data Engineering, who own the pipelines and the shared data platform, to get them implemented.

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  Frame and execute analytical work independently: define the right business question, segment patterns by product line and issue type, quantify the scale and trend of CX issues, and benchmark findings against SOTA VoC practices at companies operating at comparable feedback volume and complexity.

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  Translate evidence into decision-ready insight: produce concise, prioritized findings and recommendations for Product, Support, Ops, and Data/Engineering stakeholders — distinguishing systemic signal from noise and quantifying business impact, not just describing ticket volume.

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  Drive cross-functional metric and taxonomy alignment: establish and maintain shared CX definitions and measurement standards so that recurring issues can be tracked and acted on across teams without manual case-by-case coordination.

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  Work as one User Insight team: co-scope projects with Data Analytics and User Research so that VoC evidence, behavioral data, and research answer the same stakeholder question, rather than three teams reporting separately.

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  Use AI tooling throughout to scale efficiency: apply LLMs, clustering, and summarization tools for topic grouping, sentiment detection, and draft synthesis; document all recurring workflows so that analysis is reproducible and not dependent on tribal knowledge.

• Demonstrated hands-on analytical capability: SQL or BI platform proficiency (data validation, aggregation, segmentation, trend analysis) applied to real customer, product, or behavioral datasets — not coursework or toy projects.

• Strong analytical reasoning and problem-framing: proven ability to work through messy quantitative and qualitative evidence, identify missing information, test assumptions, and reach appropriately scoped conclusions without over-claiming.

• Relevant domain experience: prior role in Customer Insights, VoC, CX or Support Analytics, Data/Product Analytics, or Product Operations, with direct exposure to feedback data pipelines or customer evidence workflows.- Prioritization and stakeholder communication: demonstrated ability to translate complex customer evidence into concise, evidence-based findings for non-analyst audiences (Product, Ops, CX, or management) — written and verbal.

• Independent in analysis, collaborative in execution: frames and runs the analysis on their own, and adjusts recommendations to what partner teams can actually act on; maintains high standards for data quality and leaves recurring work clearly documented and transferable.

• Experience with Python, APIs, or workflow automation applied to data collection, cleaning, or reporting pipelines.

• Familiarity with AI/LLM-assisted analysis workflows (e.g., using LLMs for topic clustering, sentiment detection, or draft synthesis) and the judgment to validate rather than blindly accept AI outputs.

• Exposure to consumer hardware or consumer electronics products and the feedback patterns specific to hardware support (defect categorization, firmware-related issues, unboxing/logistics complaints).