Product Analyst II

As a Product Analyst II at HackerRank, you will own the end-to-end analytics for our products and platforms, working across AI-powered experiences, developer-facing products, content, community growth, or platform initiatives. This is not a BI reporting role — you will own and drive analytics initiatives end-to-end and act as a thought partner to Product, Engineering, Content, Design, and business stakeholders, helping answer not only "what happened?" but also "what should we build, improve, prioritize, or measure next?"

• Own analytics for next-generation product(s), platform, content, AI, community, or growth areas.
• Define and evolve metrics across adoption, engagement, retention, satisfaction, usability, usage quality, and customer value.
• Analyze customer and developer journeys, segments, and feature-level behavior to identify opportunities and friction points.
• Evaluate the quality, effectiveness, and adoption of new content and AI-powered product experiences.
• Partner with Product, Engineering, Content, Design, and Marketing teams to define measurement plans and success criteria for new initiatives.
• Drive experimentation by framing hypotheses, sizing opportunities, designing experiments, interpreting results, and recommending next steps.
• Use AI agents as part of your daily analytics workflow, and analyze AI assistants/agentic workflows including usage, outcomes, cost, and usage-based pricing.
• You have 3-6 years of full-time experience in product analytics, data science, growth analytics, consumer analytics, platform analytics, or a related field.
• You have advanced SQL skills and experience working with event, session, or clickstream-level product data.
• You are comfortable using Python for analysis and working with large, imperfect, or text-based datasets.
• You have a strong foundation in statistics, including hypothesis testing, confidence intervals, statistical significance, power analysis, sampling, regression, and causal inference.
• You understand funnels, cohort analysis, segmentation, retention, experimentation, and feature adoption.
• You can turn ambiguous business questions into structured analyses, clear recommendations, and measurable outcomes.

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