Predictive modeling and data engineering for Site Analytics.

Key initiatives:

• Developed a new traffic forecasting model for use during COVID-19 lockdown.
• Refactored queries for Tableau extracts to reduce the amount of data scanned by ~50%.
• Mentor analysts in hypothesis testing and SQL best practices.
• Leading an initiative to version and document the team’s SQL queries in Git.

“Real-Time Intelligent Audio and Object Detection for Low-Cost Robotics Systems”

A University of Chicago M.Sc. Analytics Capstone project in collaboration with R3 Robotics and Ilykei Software Corp. Developed Python software for the Dexter GoPiGo3 miniature robot car to autonomously complete a scavenger hunt with audio and visual clues. Working on a team of 5, I primarily focused on audio recognition.

• Customized efficient, mobile-optimized deep neural networks in TensorFlow 2.0: EfficientNet-Lite for audio recognition and MobileNet-SSD for object detection.
• Implemented noise-filtering DSP to reduce the impact of motor noise.
• Models were integer quantized and converted to TFLite for Edge TPU inference.
• Finished and delivered the final product remotely during the COVID-19 lock-down.

Won the Spring 2020 M.Sc. Analytics Capstone Showcase.

• Data Engineering Platforms for Analytics
• Linear Algebra

Focused on advanced analyses using raw Adobe clickstream data. Established as an analytics subject matter expert across the organization.

Key initiatives:

• Analytics lead for proof-of-concept initiatives with data vendors, including sales attribution, cross-device stitching, and enhanced lot visitation. Utilized Hadoop, Redshift, and PySpark.
• Designed a bot detection algorithm to filter bot traffic from clickstream data to enhance analyses, forecasts, and models.
• Took ownership of data engineering needs for the Site Analytics team.
• Built RShiny dashboards to evaluate the performance of multivariate tests.
• Represented Cars.com at annual Adobe Summit conferences as a speaker.

Partnered with product owners and developers to analyze user engagement, identify and monitor KPIs, and design measurement plans. Advanced from a junior-level analyst to the Adobe Analytics subject matter expert.

Key initiatives:

• Developed an analysis methodology to uncover and compare onsite shopping behavior patterns for the surveyed Cars.com Shopper Personas.
• Authored the Cars.com Visitor Analysis, a holistic view of user distribution and engagement across the site. Presented new ways of understanding and monitoring user retention and success that were adopted as KPIs by senior leadership.
• Led an initiative to overhaul Adobe Analytics tracking on the site. Forged relationships with senior developers to develop a community of practice around data quality and data governance. Established standards for tracking documentation and consulted on custom tracking libraries.
• Helped develop standards for statistically sound A/B testing.