Kyle Neary

Experience

Director of Data Science & Analytics

Great Wolf Resorts | Madison, WI | 2020 – Present

• Define data strategy and roadmap to deliver robust edge cloud-native data, analytics, and AI platforms.
• Scaled Analytics Team from 3 members to 9, including introducing Data Science and ML function through targeted hiring; justified >$1MM budget with quantified business cases, moving previously outsourced Analytics practice completely in-house, enabling technical and non-technical users to intuitively find and leverage data.
• Established process and built control metrics, monitors, and alerts to manage dynamic data platform, overseeing the integration and processing of over 100GB of data weekly from 40+ on-prem and cloud-based systems.
• Led rapid hypothesis testing in Digital environment using Optimizely to manage and analyze 10+ A/B tests per quarter, optimizing new designs and customer booking flows to drive conversion and total revenue.
• Developed and deployed Python-based revenue forecast for bookings with 20% error, beating industry-leading out-of-the-box tools, deployed directly into the data warehouse for near-realtime prediction.
• Expanded the user base of the data platform by eight times its original size, optimizing analysis time by 75% in critical areas through the prioritization and delivery of machine learning forecasting and actionable reporting.

Lead Data Scientist

Intel Corporation | Hillsboro, OR | 2017 – 2020

• Led multi-geo team of eight Data Scientists and Machine Learning Engineers to deliver analytics and data science solutions and consultation for key business objectives from multiple business units across the enterprise.
• Served on IT Data Architecture committee, pioneering the definition and prototyping of an enterprise-scale data lakehouse architecture and MLOps tools and processes, ensuring highly-integrated data pipelines and maintaining security, quality, and interpretability at a petabyte scale.
• Drove bottom-line benefit surpassing $10MM by building and executing customer segmentation, personalized marketing, and market/financial forecasting using Python, Jupyter, MLFlow, and Automated ML tools.
• Created and taught Data Science and AI courses to technical, non-technical, and executive-level audiences, providing tactical next-steps to stakeholders to build AI-enabled strategy into business processes.

Yield Analysis Process Engineer

Intel Corporation | Hillsboro, OR | 2014 – 2017

• Created SAS-based statistical tool to analyze variation in manufacturing tool performance, highlighting preferable configurations, driving yield improvements delivering $50MM in value.
• Reduced compilation time of core data tables from 6 hours daily to <10 min. through improved database design and management process, enabling low-latency analysis, reducing experiment design cadence by ~1 day.
• Developed hierarchical clustering model to identify and classify manufacturing process failures, accelerating root cause identification by reducing redundancy in testing and analysis of similar failure modes.