Product Data Analyst

Responsibilities:

  • Gather and analyze data from various sources to extract actionable insights.
  • Use statistical techniques and data visualization to interpret trends and patterns.
  • Identify opportunities for product improvements based on data analysis.
  • Monitor key product metrics and performance indicators.
  • Conduct regular evaluations to assess product performance against goals and benchmarks.
  • Identify areas of improvement and recommend strategies for optimization.
  • Collaborate with stakeholders to gather and document product requirements.
  • Translate requirements into clear and actionable user stories or product specifications.
  • Prepare reports and presentations to communicate insights, findings, and recommendations.
  • Present data analysis and product performance evaluations to stakeholders and cross-functional teams.
  • Clearly articulate complex concepts concisely and understandably.


Requirements:

  • Data Analysis: Proficiency in using analytical tools and techniques to gather, analyze, and interpret data.
  • SQL and Database Knowledge: Proficiency in SQL to extract and analyze data from databases.
  • Excel skills: Ability to prepare reports in online/offline Excel for business teams to monitor KPIs
  • Python/R: Working knowledge of Python or R to handle metadata and complex calculations.
  • Problem-Solving: Ability to identify problems, gather relevant information, and propose practical solutions to improve product performance and user experience.
  • Critical Thinking: Capacity to think analytically, assess situations, and make data-driven decisions.
  • Stakeholder Management: Skill in managing relationships with stakeholders, understanding their needs, and balancing competing priorities.
  • Teamwork and Collaboration: Ability to work collaboratively with cross-functional teams, including product managers, designers, developers, and marketers.
  • Adaptability: Flexibility to navigate a dynamic and fast-paced work environment, adjusting priorities as needed.
  • Attention to Detail: Strong attention to detail to ensure accuracy and precision in data analysis, documentation, and requirement gathering.
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