Steiner
Abayie.

I worked on marketing incrementality, campaign measurement, and acquisition analysis to inform investment decisions.

• Informed approximately $10M in marketing budget reallocation by conducting incrementality studies that identified declining ROI among influencers with more than 1 million followers.
• Saved 80 hours per month by automating campaign and commission-structure measurement with SQL and Tableau.
• Guided marketing investment discussions by presenting quarterly acquisition and incrementality analyses to the CMO and Finance leadership.

At The Sandbox, I partnered with Product and Engineering on onboarding experiments, analyzed creator journeys, and helped guide growth investment.

• Informed the launch of a new web onboarding page by partnering with Product and Engineering on an A/B test that measured a 26% reduction in abandonment, following funnel analysis across 5.7 million users.
• Guided prioritization of a fix to Game Maker's core publishing button and flow by analyzing user drop-off and identifying friction in the publishing journey.
• Established US Growth experiment measurement by defining primary metrics, sample sizes, guardrails, and decision criteria.
• Informed a $25M shift in marketing investment by segmenting users and identifying game creators as the highest-value cohort.
• Automated data ingestion for product and lifecycle analysis by building Airflow-orchestrated API-to-Redshift pipelines.

At Placer.ai, I worked on churn prediction, retention measurement, and customer segmentation, and built tools for self-service analysis.

• Measured a 4% retention lift by evaluating outreach interventions against a holdout group, informing broader adoption of the retention strategy.
• Enabled targeted retention outreach by developing a Python and scikit-learn churn model using product usage and account features and deploying scores into a recurring GTM workflow.
• Supported customer segmentation and account prioritization by building SQL analytical tables from Salesforce account, opportunity, contact, and activity data.
• Enabled self-service analysis by building a Slack and Deepnote assistant that generated SQL from natural-language questions and returned answers from BigQuery.

As a contractor at Johnson & Johnson, I automate finance reporting, improve data reliability, and build process-documentation tools for the finance data team.

• Saved 100+ hours per month by automating reporting with SQL, Python, and Alteryx across millions of records, four global finance processes, and 39 business units.
• Reduced recurring data capture errors by 60% by implementing validation, reconciliation, and data governance controls to improve downstream reporting reliability.
• Reduced repetitive support requests by launching a Copilot process-documentation agent for the finance data team.