Head of Data Science, Square Point of Sale and Health & Beauty Audience.- Led the creation of various deep learning and natural language processing solutions for up-leveling merchant categorization, powering company-wide KPIs.- Developed org-level ML, experimentation, metrics, and data engineering strategy for Square's unified point of sale system and Health and Beauty merchants, significantly increasing incremental revenue.Previously: Senior Data Science Manager for Square Appointments, Conversations, and Go.- Directly drove data science strategy and workflows for two 0 -> 1 product launches.- Tackled challenging business problems ranging from the seller growth lifecycle and messaging platforms to buyer-facing marketplace growth, driving significant incremental revenue.- Managed both data scientists and machine learning engineers; reported directly to a General Manager.

Engineering Manager - Data Science, Anti-fraud Transaction Profiling team.- Tackled fraud detection problems across ACH transfers, debit card, crypto, and emerging fraud spaces, generating tens of millions of incremental fraud loss savings.- Contributed to Robinhood's ML model production workflows (e.g. holdout groups, feature stores, model retraining, etc.).- Drove extensive collaborations with Fraud Operations, Security, Finance, Customer Care, Risk, Financial Crimes, Data Engineering, ML Platform, and multiple product teams.- Scaled number of reports from 3 to 10; reported directly to the Robinhood Head of Data.Previously: (First) Senior Data Scientist on the Anti-fraud team.- Built ML models for reducing first-party fraud on the Robinhood platform.- Led efforts to up-level ML tooling / processes, experimentation, and fraud metrics tracking.

Senior Data Scientist on the Expansion (Growth) team.- Operationalized new metrics at the company-level to guide product strategy.- Developed upgrade prediction model (PySpark) to guide targeted promotion campaigns.- Contributed to the improvement of the internal A/B testing framework.

Data Scientist - Algorithms on the Trust (Financial Fraud) team; Data Scientist - Inference on the Payments (Guest Commerce) team.- Developed real-time machine learning models to help Airbnb reduce fraudulent chargebacks, generating millions in incremental fraud loss savings.- Built scalable data pipelines to measure the performance of fraud defenses, projected chargeback rates, etc.- Designed and analyzed A/B tests to provide Product team with actionable insights.

- Course instructor for MS&E 121 Introduction to Stochastic Modeling (undergraduate).- Teaching assistant for MBA course on optimization, simulation, and modeling.- Teaching assistant for Ph.D. courses on revenue management, stochastic systems, and stochastic calculus.- Case study research for online executive education.

Three consecutive summers as an undergraduate research student at the Department of Mathematics and Statistics, Queen's University, tackling projects revolving around:- Stochastic simulation and queueing theory.- Random matrices and free probability.- Analytic number theory.