Marketing Analyst

Rakuten Rewards•Toronto, ON

6d•CA$55,456 - CA$85,923•Onsite

About The Position

Rakuten International is a division of Rakuten Group, Inc., a Japanese global technology leader in services that empower individuals, communities, businesses and society. Headquartered in San Mateo, California with more than 4,000 employees worldwide, the Rakuten International business portfolio includes market leaders in e-commerce, digital marketing, advertising, communications and entertainment. We create products and services that provide exceptional value by aligning members and the businesses that want to engage them in a shared community. Rakuten is a leading shopping platform that offers Cash Back on purchases from your favorite brands. By partnering with thousands of brands in apparel, beauty and wellness, dining, grocery, travel, on-demand services, subscription boxes, and more, Rakuten helps members save and get more on everyday purchases. Since its founding in 1999, Rakuten has become the largest and most rewarding shopping program, and its members have earned $4.6 billion in Cash Back just for shopping through Rakuten. For more information, visit Rakuten.com. Job Summary: We are looking for a results-driven Data Analyst who can transform complex data into clear, actionable insights that support strategic and operational business decisions. The ideal candidate brings strong analytical thinking, hands-on experience with SQL and Python, and the ability to communicate findings clearly to both technical and non-technical stakeholders. You thrive in ambiguous environments, can move fluidly between business questions and technical execution, and are comfortable using modern AI-assisted tools to improve analytical speed, code quality, and workflow automation.

Requirements

2–5 years of experience in data analytics, data science, or a related role
Advanced SQL proficiency (complex joins, window functions, performance optimization)
Strong Python skills for data analysis and data science (pandas, numpy, scikit-learn)
Solid understanding of statistics, probability, and analytical best practices
Experience with experimentation, hypothesis testing, or causal analysis
Hands-on experience with BI and visualization tools (Tableau, Power BI, ThoughtSpot, etc.)
Proven ability to translate business questions into analytical frameworks and communicate insights clearly
Practical experience using Claude Code or similar AI-assisted coding and analytics tools, with the ability to critically review and validate AI generated output.

Nice To Haves

Hands-on experience building and validating machine learning models (regression, classification, clustering, time-series forecasting)
Experience supporting ML workflows- feature engineering, model selection, validation, and performance tracking
Familiarity with deploying or monitoring ML models in production environments
Experience with cloud data warehouses (Snowflake, BigQuery, Redshift)
Background in e-commerce, marketing, product, or growth analytics
Experience building more scalable, automated, and AI-enabled analytics workflows.

Responsibilities

Own and monitor key business and marketing KPIs, tracking performance and conducting deep-dive analyses to identify trends, risks, and growth opportunities
Translate business problems into analytical frameworks and deliver clear, actionable recommendations to technical and non-technical stakeholders
Write efficient, scalable SQL queries to extract, transform, and validate data from multiple sources, ensuring metric accuracy and consistency
Perform exploratory data analysis and statistical modeling using Python to inform strategic and operational decisions
Apply statistical techniques to support experimentation, A/B testing, and causal analysis
Build and maintain dashboards and automated reporting using BI tools (Tableau, Power BI, Looker) and develop scalable, reusable analytical assets
Build, validate, and interpret machine learning models, including segmentation, propensity modeling, forecasting, and predictive scoring
Evaluate and apply AI tools responsibly, ensuring analytical outputs are accurate, explainable, reproducible, and aligned with business context.