Senior Data Scientist, MagicSchool AI

• MagicSchool is forming a growth team to accelerate our expansion, and we need a data scientist who understands what drives user growth
• 5+ years in data science or analytics roles
• Growth team experience at a product-led company
• Experience with growth, product, or marketing analytics
• Strong SQL skills and proficiency in Python
• Understanding of statistical testing and experimental design
• Experience with subscription or SaaS business models
• Track record of driving business impact through analysis

Desirable

• You'll work at the intersection of product, marketing, and sales to uncover insights that help us activate, engage, and retain our rapidly growing educator community
• This role combines rigorous analytics with growth strategy. You will build models that predict user behavior, design experiments that test growth hypotheses, and create the measurement infrastructure that guides our growth investments
• Analyze user funnels to identify drop-off points and optimization opportunities
• Build cohort analyses (or similar)
• Understand retention patterns and user lifecycle
• Work with product and marketing to develop segmentation strategies
• Create predictive models for user activation, conversion, and churn
• Design dashboards that track growth metrics and experiments
• Partner with product and marketing teams to design A/B tests
• Analyze experiment results with appropriate statistical methods
• Document learnings and build institutional knowledge
• Develop growth product roadmaps based on impact and effort
• Define and instrument key growth metrics (activation, engagement, retention)
• Build regression-based product attribution models that connect user behaviors and characteristics to retention, health scores, and product engagement outcomes
• Develop health scores and user quality metrics
• Create early warning systems for user churn
• Track and analyze both self-serve and sales-assisted conversion paths
• Collaborate with engineering on data infrastructure and instrumentation
• Support marketing with campaign analysis and audience selection
• Enable sales teams with lead scoring and account intelligence
• Partner with product on feature impact analysis
• Communicate insights to stakeholders at all levels
• Technical Stack:
• SQL for data analysis and pipeline development
• Python for statistical analysis and modeling
• BI tools (Looker, Tableau, Metabase)
• Product analytics platforms (Amplitude, Mixpanel)
• Cloud data warehouses (BigQuery, Snowflake)
• Basic understanding of data engineering concepts