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Sr. Product Analyst

Sr. Product Analyst

CompanyRakuten
LocationToronto, ON, Canada
Salary$Not Provided – $Not Provided
TypeFull-Time
DegreesBachelor’s
Experience LevelSenior

Requirements

  • 4+ years of professional experience as a product analyst, preferably in the retail or e-commerce industry.
  • Strong problem-solving and critical-thinking skills.
  • Strong SQL skills, comfortable working with large datasets (e.g., financial, clickstream) in a Big Data environment.
  • Strong understanding of A/B testing methodologies and statistical analysis.
  • Experience with reporting and visualization tools (Tableau preferred).
  • Ability to work in a fast-paced environment, manage multiple projects of different contexts and delivering on time.
  • Strong interpersonal skills, both written and verbal, with the ability and confidence to succinctly convey complex information to senior management.
  • A bachelor’s degree in computer science, mathematics, economics, statistics, or engineering is required.
  • Proficiency in applying statistical approaches to test design, measurement, and analysis.
  • Skills in translating data-driven learnings and statistical models into actionable insights and effectively communicating these to key stakeholders.

Responsibilities

  • Collaborate with Product and Engineering teams to identify opportunities, estimate impact to prioritize focus, implement and analyze tests.
  • Define key business metrics for experimentation and run large scale experiments that deliver impactful results to the business.
  • Build scalable reporting systems using SQL, BI tools, and scripting languages that help provide insights to business stakeholders.
  • Perform deep dive analysis to explain what happened and recommend improvement opportunities.
  • Be the expert on the data sets and data tools available to improve the product experience using data.
  • Help monitor the quality of the data that is being made available through the company’s reporting tools.

Preferred Qualifications

  • Experience working with experimentation platforms (e.g., Optimizely, LaunchDarkly) is preferred.