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We are looking for a hands-on manager for our product analytics team. You will collaborate with company leadership, product managers, engineers, marketers and researchers to accelerate learning, make data-informed decisions and define data-inspired solutions to fuel the growth of Strava’s platform. This role reports to the head of data and partners tightly with cross-functional stakeholders throughout the company.
Job Responsibility:
Lead a team of data analysts to support the diverse needs to the Product Org team, focusing on user and subscription growth
Drive strategic analytics initiatives to improve the efficiency and impact of the growth of Strava’s community and subscription product
Establish a learning agenda to create a foundation for robust product and business growth strategies
Partner with product and marketing teams to design and interpret A/B tests to drive explainable user and subscription growth outcomes
Collaborate with the broader data community at Strava (Data Science, Machine Learning, Data Platform, etc) to collectively improve our technological craftsmanship and company-wide data literacy
Conduct deep dive analyses to surface actionable insights related to trends in key business metrics
Partner with product, biz ops, and finance teams to support annual business planning and product team goal settings
Leverage your quantitative skills and business background to serve as a hands-on collaborator with vertical teams within Product, Strava for Business, and Community Management
Think about scalability, building reusable data sets, and designing self-service tools to empower your collaborators to learn along with you
Not being afraid to ask questions, learn, share and iterate on ways of working, your business area, and analytics capabilities
Requirements:
7+ years of full-time experience in analytics, data science, or other quantitative domains and have supported product teams
4+ years of experience leading high-functioning analytics teams
Master’s degree in quantitative field preferred
High proficiency with SQL and experience with Business Intelligence tools (e.g. Tableau)
Expertise applying experimentation and advanced statistical methods to measure incremental impact across product strategies
Hands-on experience working with statistical programming languages (e.g. R, Python)
An understanding of data pipeline concepts (e.g. ETL, scripting common analysis workflows)