Senior Product Data Analyst at Introhive, an AI-powered relationship intelligence platform. Analyzing data to derive insights and improve product outcomes within the product operations team.
Responsibilities
Analyze user behaviour and outcomes to drive decisions
Run funnel, cohort, retention, time-to-value, and feature adoption analyses; pinpoint drop-offs and root causes.
Segment analytics by persona, account tier, industry, and/or region to illuminate who succeeds (or struggles) and why.
Combine quantitative signals with VoC (Voice of the Customer) data to produce decision-ready recommendations with expected impact.
Own product analytics platform & metrics framework
Administer and evolve Pendo: event taxonomy, instrumentation to close gaps, governance, and data quality.
Define standards for key product metrics at the product/feature level.
Build and maintain dashboards for product health and usage, ensuring quality and self-serve capabilities.
Drive VoC & GTM feedback loop data strategy
Automate ingestion of product insights, unifying inputs from GTM, support, surveys, and call transcripts.
Implement simple data pipelines to manage data in a robust, repeatable way, ensuring clear lineage and high data quality.
Implement taxonomy and process for feedback to be efficiently categorized, summarized, and prioritized in a data-driven manner.
Implement AI workflows (e.g., topic/intent extraction, clustering, summarization, anomaly detection) where feasible, with human-in-the-loop review and appropriate compliance guardrails.
Contribute to the product commercialization process by helping to define and measure product outcome metrics for product investments to test hypotheses early-on, then to assess ROI on Beta/GA launches.
Provide portfolio-level recommendations—what to scale, fix, or stop—based on impact and confidence.
Conduct analysis to identify opportunities and validate direction for both near-term product improvements and larger strategic bets.
Deliver self-serve dashboards, and/or training to raise analytics fluency; and help GTM team members use analytics as part of renewal and expansion motions.
Collaborate with product managers to design hypothesis-driven experiments to increase activation, stickiness, and expansion; define success and guardrail metrics, do analysis, and make recommendations.
Collaborate with product managers to identify and test growth levers with measurable outcomes.
Requirements
5+ years of experience in data analysis, product operations, or adjacent roles
Bachelor's degree in Computer Science, Engineering, Statistics, Business, or a related field
Expertise in product analytics (funnel, cohort, retention, segmentation), experimentation, and causal thinking.
Proficient with SQL, a BI tool (e.g., Tableau/Looker/Power BI), cloud data warehouses (e.g., Snowflake), and at least one product analytics platform (e.g., Amplitude/Mixpanel/Pendo) or equivalent.
Skilled at using LLMs and AI tools to accelerate analysis and operations (e.g., generating SQL/code safely, summarizing usage/VoC, clustering themes, triaging feedback, and narrating dashboards).
Comfortable combining quantitative signals with qualitative research to tell a cohesive story.
Exceptional at building decision-ready narratives—clear visuals, plain language, and crisp recommendations.
Experience defining event schemas, tagging strategies, and ensuring data quality at scale.
Strong communication and stakeholder management; able to align PM, Engineering, UX, CS, and GTM around metrics and insights.
Familiar with hypothesis design, guardrail metrics, and statistical concepts (e.g., confidence intervals, p-values, power).
Pragmatic, organized, and with demonstrated experience managing multiple parallel initiatives and prioritizing effectively.
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