• Own product data strategy & reporting automation: develop and implement scalable data solutions that streamline reporting and analytics. • Perform quantitative analysis: deep dives into product and business data to unlock insights and opportunities. • Support hypothesis testing: collaborate with Product Managers to validate product hypotheses with rigorous data analysis. • Data analysis & insights: analyze large datasets to identify patterns, trends, and key insights related to user behavior, product usage, and market dynamics. • Reporting & visualization: build and maintain dashboards and reports to monitor KPIs and communicate findings effectively. • Self-service dashboards & data literacy: create and maintain self-service dashboards to democratize data access; lead "brown bag" sessions to educate teams. • Data quality assurance: ensure data accuracy and consistency, partnering with relevant teams to resolve data issues. • Forecasting & predictive analytics: develop forecasting models to estimate product performance and uncover growth opportunities. • Cross-team collaboration: work closely with the business data science team while maintaining focus on product organization’s data needs. • Expected impacts: improved decision-making, cohesive data structure, higher feature success rates, faster iteration cycles, increased data literacy.