Senior Data Analyst (Marketing Analytics)

Since 2018, Aviron has been on a mission to transform exercise into an experience people love. Driven by our commitment to innovation, we've continuously evolved our products to deliver solutions that not only make working out fun and accessible, but aim to change the way people feel about their next workout. From our pioneering gamified rower to the latest cutting-edge Aviron Fit Bike, we continue to push the boundaries of what's possible in fitness, ensuring every launch brings us closer to our mission to End Boring Workouts. About the RoleIn this role, you will play a crucial part in driving data-informed decisions across the organization. You will be responsible for collecting, cleaning, analyzing, and interpreting complex datasets to identify trends, patterns, and insights that will inform business strategy and improve performance.  This individual will report directly to the Chief Revenue Officer.  Responsibilities Data Collection and Preparation: Gather data from various sources (databases, APIs, web analytics, etc.), clean, transform, and prepare it for analysis. Data Analysis and Interpretation: Conduct in-depth analysis using statistical methods and tools to identify trends, patterns, and correlations. Interpret findings and translate them into actionable insights. Business Intelligence: Develop and maintain business intelligence dashboards and reports to track key performance indicators (KPIs) and provide insights into business performance. Testing Framework: Develop and maintain a robust A/B testing framework, including best practices for experiment design, sample size calculation, and statistical analysis. Collaboration and Communication: Partner with cross-functional teams (CRM, Growth Marketing, Digital Product) to understand their data needs, provide data-driven recommendations, and communicate findings effectively. Data Visualization and Reporting: Create clear and compelling visualizations (dashboards, reports, presentations) to communicate data insights to both technical and non-technical audiences. Data Quality Assurance: Implement and maintain data quality checks to ensure data accuracy and reliability.