• Own end-to-end analytics for the signup, activation, retention, and cancellation funnels across Scribd’s brands, identifying friction points and opportunities for growth.
• Lead event instrumentation and taxonomy strategy for the growth pillar, including defining event requirements, maintaining tracking documentation, and partnering with engineering to ensure accurate and consistent data collection to measure touchpoints within the signup flow and cancellation flow.
• Design and execute end-to-end experimentation frameworks to measure and optimize growth initiatives across the funnel, including hypothesis generation, test design, metric definition, analysis, and readouts. Partner with Data Science and Product to establish experimentation best practices, set success thresholds, and ensure statistical rigor in all A/B tests
• Collaborate with Product, Lifecycle, and Data Engineering teams to design experiments, define success metrics, and measure the impact of product and marketing initiatives.
• Build and maintain scalable dashboards and reporting frameworks in Looker and Databricks to track growth KPIs such as signups, trial conversion, bill-through rate, churn, and LTV
• Partner with Growth PMs to identify and prioritize opportunities through funnel and cohort analyses, experimentation, and user behavior insights
• Support scenario planning and impact sizing for key growth initiatives, helping quantify potential outcomes, model tradeoffs, and inform prioritization decisions across acquisition, activation, and retention efforts
• Ensure data quality and governance across tracking systems, improving event coverage and visibility for key flows
• Mentor analysts and help establish best practices for product analytics, experimentation, and taxonomy management

Requirements

3+ years of experience in product or growth analytics, ideally within a high-velocity, subscription-based, or consumer-facing environment
Advanced proficiency in SQL and Python or R for data querying, analysis, and experimentation support
Deep hands-on experience with BI and analytics tools such as Looker, including building and optimizing dashboards, maintaining LookML models, and establishing data governance standards
Experience with data warehouses such as Databricks, including proficiency in data modeling, performance optimization, cost management, and collaboration with data engineering to maintain efficient and reliable pipelines
Proven experience defining event schemas, designing tracking taxonomies, and collaborating with engineering on instrumentation
Solid knowledge of A/B testing design, hypothesis testing, regression analysis, and funnel analysis, with a demonstrated ability to translate results into actionable recommendations.
Experience designing and analyzing incrementality testing to measure true growth impact across experiments, campaigns, and lifecycle initiatives
Experience with Customer Data Platforms (CDPs) such as Segment, mParticle, or RudderStack
Excellent communication and presentation skills, with the ability to translate complex findings into clear, actionable recommendations
Bachelor's degree in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or a related discipline. A Master's degree is a plus.

🔍 ATS Optimization Keywords
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Hard Skills

SQL
Python
R
A/B testing
regression analysis
funnel analysis
data modeling
event schemas
tracking taxonomies
incrementality testing

Soft Skills

communication
presentation
mentoring
collaboration
analytical thinking
problem-solving
prioritization
data governance
hypothesis generation
actionable recommendations

Certifications & Qualifications

Bachelor's degree in Statistics
Bachelor's degree in Mathematics
Bachelor's degree in Computer Science
Bachelor's degree in Economics
Master's degree (preferred)

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