Product Data Analyst

The Cricut Analytics Team is looking to hire an outstanding Data Analyst with strong technical analytical skills to operationalize data intelligence in multiple business units. Person is accountable for executing high-impact analytics initiatives, creating, and driving a culture that values and understands the importance of data. This person will collaborate very closely and cross-functionally with Product Marketing, Product Management, Operations as well as the Analytics team, to ensure success. You will also be responsible for making data-driven recommendations to business partners on improving customer engagement/satisfaction in using our software platform.Data teamWe are looking for highly talented team members who are passionate about data, have the rigor needed to solve billion-dollar problems, and possess an innate entrepreneurial spirit to explore the uncharted. This team combines the engineering backbone of a best-in-class big data platform with the analytic expertise of advanced mining and predictive modeling. If you want to work among the very best talent in the industry, working on the most innovative products in the world, Cricut is the place to be.As a Product Analyst, you’ll:Duty/ResponsibilityPercentage of Time30% - Actionable Insights: Develop strategies and provide direction for the application of our BI/Dashboarding tool, and build robust KPI's and drill-down capabilities to enable discovery of actionable insights30% - Communication: Collaborate with data science, data engineering and business team to deliver features that will make an impact to company North Star metrics20% - Domain Expert: It is essential that this role has the capability to understand business function and its nuances, and define proper KPIs to measure health & success of the business10% - Data Owners: The person in this role will be the data champion for a specific business function, and the single point-of-contact for internal & external teams on questions related to their business unit’s datasets10% - Measurement & A/B testing: Build relevant KPIs to monitor the effectiveness of Data Science models. Create a multivariate testing and machine learning culture to unlock value and drive continuous improvement.