Tom is an experienced data modeler with a decade of experience in the technology sector. He's worked across the data stack in various roles, including data scientist, analytics engineer, and data engineer. His professional experience includes data roles at Facebook and Miro, where he led data warehousing, data modeling, and data platform initiatives.
Built and managed data infrastructure for a program's in-house CRM platform. Data infrastructure was used for sourcing new leads, monitoring program efficiency, and integrating data sources.
Managed analytics for a new, experimental product developed for small businesses, including targeting criteria, feature-flags used during release, and developing insights about product adoption.
Built and maintained lead-scoring models for an on-platform campaign that drove a 30% improvement in cost per campaign objective.
A greenfield Docker-based Airflow deployment used for building out the company's first data pipelines. The Airflow app managed ETL jobs that connected the company's internal services and vendor platforms with their data warehouse. The app was also responsible for calculating the company's KPIs including daily active users, performing lead-scoring of new users, and monitoring the quality of data ingestion. The data pipelines in the application were used for several mission-critical projects, including A/B testing new pricing models and validating the effects of new feature releases of the company's customer-facing applications.
Built a lead scoring system that automatically ranked all new users based on their propensity to become qualified leads, and then allocated the most qualified leads to the sales team each day for further qualification. The system used signals from data enhancement sites like Clearbit, engagement with marketing content on the company's website, and product usage to help improve the efficiency of our inside sales team and lead nurturing campaigns.
Built data analytics, including data pipelines and statistical significance calculations, for A/B tests of the company's pricing plans and feature releases. Provided recommendations based on that analytics which significantly affected the company's pricing strategy and product roadmap.
