Yingwei Z.
Senior Analytics Engineer @ACV Auctions
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WORK HISTORY
Senior Analytics Engineer @ACV Auctions
Product & Data Architecture: Architect scalable BigQuery data models for the auto finance portfolio, establishing a governed single source of truth. Lead end-to-end analytics for the Capital Operations Portal (COP), partnering with Product and Engineering to instrument event tracking, define KPIs, and productionize reporting frameworks• ML & Risk Model Innovation: Developed and deployed a flagship Account Management Model (Python, BigQuery, Airflow) with SHAP-based interpretability; reduced risk review time by 70% and enabled the team to manage 2x dealer volume in support of a 100% YoY revenue growth target- Impact Measurement & Productionization: Design evaluation frameworks to quantify incremental impact of new portal features using cohort analysis and quasi-experimental methods. Deploy production-ready dashboards (Tableau, Omni) and version-controlled ETL pipelines (GitHub) to ensure scalable, reliable decision support• Technical Leadership: Scaled the Analytics Org by delivering ML training programs and standardized Python templates; mentored junior engineers on Airflow implementation, code reviews, and PDLC best practices to ensure high-velocity, reliable delivery
EDUCATION
University of Waterloo
Master’s Degree, Statistics
Stony Brook University
Bachelor’s Degree, Applied Mathematics and Statistics, Economics (2nd Major)
SKILLS
ABOUT YINGWEI Z.
Data is only as good as the decisions it drives. I am a Senior Data Scientist & Analytics Engineer with a Master’s in Statistics and 8+ years of experience building production-grade analytical systems. I thrive at the intersection of data architecture, statistical inference, and machine learning—turning ambiguous product questions into scalable systems and measurable impact.My Core Focus: Scalable Data Architecture – Building the foundation with governed BigQuery models, dbt transformations, and Airflow pipelines. I believe in a \"single source of truth\" backed by engineering best practices.🧪 Product Experimentation & Causal Inference – IMoving beyond \"what happened\" to \"why it happened.\" Whether it’s A/B testing, cohort analysis, or counterfactual thinking, I focus on measuring incremental lift to drive roadmap decisions.🧠 Interpretable ML Systems – I build \"Glass Box\" models. Using explainability frameworks like SHAP, I provide the \"why\" behind the data to support high-stakes decisions in both regulated and high-growth environments. Operational Impact – From my work at ACV Auctions leading Marketplace and Capital initiatives to my time building IFRS9/CCAR models at TD Bank, I bridge the gap between technical output and executive strategy.I\'m especially interested in- Marketplace & Product Analytics at scale- Causal Inference & Quasi-experimental design- Mentoring engineers to think like product owners- Turning technical ambiguity into measurable business outcomes.Let’s connect if you’re passionate about building data cultures that value reliability as much as speed :)
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