Ryan

Ryan is Adaptly’s Data Science & Analytics Lead, combining deep statistical expertise with practical business application. He specializes in building machine learning systems that automatically optimize user experiences through data-driven personalization.

Background

With a background in applied mathematics and 7 years in data science, Ryan has worked across fintech, ecommerce, and martech industries. His passion lies in making complex statistical concepts accessible to marketing teams while building robust ML systems that scale.

Technical Expertise

• Statistical Analysis: Expert in experimental design, hypothesis testing, and statistical significance
• Machine Learning: Builds and deploys personalization algorithms, recommendation engines, and predictive models
• Data Engineering: Designs scalable data pipelines for real-time personalization and analytics
• A/B Testing Analytics: Creates frameworks for proper test design, power analysis, and results interpretation
• Customer Analytics: Develops segmentation models and lifetime value predictions

Statistical Philosophy

Ryan believes that data should tell a story, not just show numbers. His approach focuses on making statistical insights actionable for business teams while maintaining mathematical rigor and avoiding common analytical pitfalls.

Specializations

• Personalization Algorithms: Develops ML models that automatically adapt content and experiences to individual users
• Test Design & Analysis: Creates statistically sound testing frameworks and interprets results with business context
• Predictive Analytics: Builds models for customer behavior prediction, churn prevention, and conversion forecasting
• Real-time Analytics: Designs systems that process and act on user behavior data in milliseconds
• Attribution Modeling: Develops multi-touch attribution systems that accurately measure marketing impact

Content Focus

Ryan demystifies data science for marketing professionals through:

• Statistical concepts explained in plain English with practical examples
• Deep dives into how personalization algorithms actually work
• Best practices for A/B test design and avoiding statistical errors
• Case studies showing how data science drives business results
• Reviews of analytics tools and their proper application

Innovation Areas

Ryan is currently working on cutting-edge applications of:

• Zero-party Data Personalization: Using explicit user preferences to enhance ML models
• Contextual Bandits: Advanced algorithms that balance exploration and exploitation in real-time testing
• Privacy-First Analytics: Building personalization systems that work within evolving privacy regulations
• Cross-Device Attribution: Connecting user journeys across multiple devices and touchpoints

Research & Development

Ryan regularly contributes to the data science community through:

• Open-source analytics tools and testing frameworks
• Research papers on applied machine learning in marketing
• Speaking at data science and marketing conferences
• Collaboration with academic institutions on personalization research

Mission

Ryan’s mission is to bridge the gap between advanced data science and practical marketing application. He believes that sophisticated analytics should enhance human decision-making, not replace it.