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Weekday AI (YC W21)

Product Analyst (Product Optimization)

Bengaluru, Karnataka, India

9 days ago
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Summary

This role is for one of Weekday's clients

Min Experience: 2 years

Location: Bengaluru, Bangalore

JobType: full-time

Requirements


Role Overview:

We are seeking a Product Analyst to take ownership of key metrics, reporting, and experimentation within Nanonets. Working directly with our founders, this role involves goal setting, tracking, and prioritizing features based on metrics-driven insights. As a Product Analyst, you'll be critical to helping teams set data-informed goals, clarifying the key metrics that drive success, and enabling a hypothesis-driven approach to product development.

Key Responsibilities

  • Metrics Ownership: Manage and report on company metrics, setting measurable goals and tracking progress against them
  • Outcome Definition: Help teams define data-backed goals and establish measurable outcomes, ensuring efforts align with strategic company objectives
  • Input Metrics Clarity: Identify and communicate the key levers that drive output metrics, providing teams with actionable insights
  • Hypothesis-Driven Development: Build systems that support hypothesis testing through product features, creating, running, and measuring A/B tests and experiments to validate customer behavior hypotheses
  • Product Development Optimization: Partner with product teams to establish high-quality goals, focus on the most impactful metrics, and support agile development based on data-driven decision-making


What We're Looking For

  • Education: Bachelor's degree preferable from a Tier 1 college
  • Technical Skills:

○ Proficiency in SQL (required).

○ Strong analytical skills and understanding of key product metrics.

○ Willingness to learn Python and enhance data engineering skills.

○ Robust product sense, with the ability to interpret and act on data for feature prioritization.

  • Mindset: Curious, experiment-driven, and eager to tackle complex product challenges through data. Comfortable scrapping or iterating on underperforming features based on data

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