Rahul Srivastava

Leading marketing science, analytics, and experimentation across the Google Cloud Learning ecosystem, partnering with product, content, and leadership teams to measure impact, optimize engagement, and guide investment decisions.

• Built and led measurement and experimentation strategy to evaluate content, experience, and launch performance, informing prioritization and investment decisions at scale.
• Drove the successful launch of Google Skills, achieving 1M+ signups and 750K+ learners in the first month through disciplined test design, sequencing, and performance tracking.
• Designed and operationalized self-serve analytics, dashboards, and metric layers used by product, partner, and leadership teams to track MAU, retention, and engagement drivers.
• Led advanced analytics and data science initiatives that quantified the relationship between learning engagement and Google Cloud consumption, demonstrating 2–3× higher likelihood of increased usage.
• Partnered cross-functionally with product, UX, and partners to translate insights into roadmap changes, content strategy, and ecosystem investments.Scaled platform reach to 250M+ learners by building and aligning a multi-partner ecosystem (DeepMind, Grow with Google, AI Boost Bites, and others) around shared platform goals.
• Built an integrated, self-serve data, analytics, and experimentation capability supporting 300+ internal and partner users, enabling OKR tracking, real-time performance visibility, and data-informed product decisions.
• Led data science and experimentation initiatives that quantified the impact of learning engagement on MAU, retention, and Google Cloud consumption (showing 2–3× higher odds of increased usage), directly informing roadmap prioritization and investment decisions.

Established analytics, measurement, and experimentation capabilities for Azure learning and engagement platforms, serving as a strategic advisor to product and business leaders.

• Defined platform-wide measurement frameworks integrating product, engagement, and financial metrics to guide executive decision-making.
• Scaled the Azure learning platform to 100M MAU by embedding analytics and experimentation into planning, launch, and optimization cycles.
• Modernized analytics and reporting infrastructure, reducing costs by 40%, improving performance by 90%, and tripling active decision-makers.
• Applied advanced analytics and early AI-driven insights to surface trade-offs, growth levers, and efficiency opportunities.
• Delivered measurable business outcomes, including $5M+ in cost efficiencies and a +3-point CSAT improvement.

Led analytics and applied data science initiatives supporting 100+ customer- and sales-facing products and 200K+ monthly active users. Built scalable analytics frameworks and experimentation platforms that improved insight velocity and enabled product teams to make faster, data-informed roadmap and execution decisions.