Product Analyst

Analyst

Product Analyst

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  • Date posted
    August 10, 2026
  • Expiration date
    November 10, 2026
  • Application ends
    November 10, 2026

As a Product Analyst , you’ll turn complex data into rigorous analysis that informs product decisions across our machine learning-powered platform. You’ll own the analytical needs of your product team end to end: identifying the right questions, executing the analysis, and presenting results that shape what gets built. You’ll work closely with Product Managers, Data Scientists, and Machine Learning Engineers to define success metrics, evaluate experiments, and understand the drivers of user behavior and advertising performance.

Responsibilities:

  • Source, scope, execute, and present analyses for your product team, working independently from question to recommendation.
  • Partner with Product Managers and ML teams to define and monitor key success metrics for product features.
  • Design, analyze, and interpret A/B tests and other experiments with statistical rigor.
  • Build and maintain dashboards and reports that enable transparent, self-service monitoring of product performance.
  • Develop reusable SQL, analytical notebooks, and lightweight frameworks, including LLM-assisted workflows, to streamline recurring analyses.
  • Investigate unexpected changes in product or business performance and clearly communicate findings and recommendations.
  • Build a strong working knowledge of  core data tables, your product area, and the wider product suite.
  • Collaborate with Data Scientists and Data Engineers to ensure data quality, metric consistency, and reliable instrumentation.
  • Document analytical approaches, assumptions, and methodologies to support reproducibility.
  • Other duties as assigned.

Requirements: 

  • 5+ years of experience in analytics
  • Strong SQL skills and proficiency in Python for data analysis.
  • Experience delivering analytical projects independently, from scoping through to presentation.
  • Strong communication skills, with the ability to explain analytical results to both technical and non-technical audiences.
  • Familiarity with BI and visualization tools (e.g., Looker, Tableau, Hex, or similar).
  • Degree in a quantitative field such as Statistics, Mathematics, Economics, Computer Science, or Engineering, or equivalent practical experience.
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