First Data Scientist at Rec Room. I currently lead the content discovery team, responsible foridentifying which of the millions of user-created rooms we should recommend to players. Right now I'm focused on: • Leading the transition from heuristic ranking -> multistage personalized ranking (feature engineering, building and deploying candidate generators and scoring models, online and offline evaluation, tuning of value models). This work as resulted in >2x increase in time spent on recommended content and significant increases in user retention as measured via longterm holdouts. • Developing our internal switchback testing framework to measure the network impact of matchmaking algorithm changes. • Promoting a culture of hypothesis-driven design and experimentation across all teams at the company (team has gone from 0 to 150+ experiments run in the last year). • Growing the data science team through hiring, onboarding, and mentoring other data scientists (team has grown from 1 -> 5 DS).

Data Science Lead for Instacart's 15 minute Ultrafast Delivery service.Selected Projects:• Optimized inventory selection for nano-fulfillment centers to maximize order coverage with limited shelf space.• Created a simulation framework to identify opportunities to reduce fulfillment times by re-organizing locations of products on shelves. • Forecasted items sales & built tool for Ops teams to manage daily replenishment of initial ultrafast store locations.

Managed and tech led a team of five data scientists working on FB News, leading analytics efforts across product growth, UI experimentation, and algorithmic ranking. Selected Projects:• Defined and implemented a suite of both engagement and non-engagement metrics to measurethe relevance of personalized news recommendation algorithms.• Identified levers for improving relevance across all stages of ranking (content understanding, candidate generation, heavy ranking, post-ranking diversity pass).• Built a model to predict future engagement volume of nascent Facebook posts; the introduction of this “Trending Score” into candidate generation improved clickthrough rate by +70%. • Forecasted goal targets and defined KPIs to measure success of a 0->1 product.• Designed and analyzed experiments to improve the core user experience, improve retention, and drive efficient growth, which has resulted in an average 70% MoM growth rate since launch.• Hired, onboarded and mentored a team of both junior and senior data scientists.

Data Science tech lead for 3 programs within Uber’s Driver Experience team, leading a team of 3 data scientists.Selected Projects• Developed internal tool to measure GPS accuracy of driver devices. Used by operations to set minimum device requirements for driver on-boarding.• Built predictive models to understand driver churn and driver sentiment for prioritizing team roadmaps. • Analyzed mobile logs to understand how drivers interacted with the app to improve usability during the core on-trip experience. Designed and analyzed experiments for a variety of feature launches. • Built and managed ETLs in Airflow. • Taught internal training classes on R and Shiny during new hire orientation.• DS tech lead for 3 programs within Uber's Driver Experience team. Past Positions: Data Scientist II: March 2015 - March 2017

Senior analyst with experience in predictive modeling, stochastic reserving, reserving for asbestos liability, reserving for construction defect liability, as well as general P&C reserving for both corporations and insurance companies. Selected project experience includes:Predictive Modeling• Developing multivariate personal auto pricing models for a CA-based insurance company• Reviewing homeowners by-peril GLMs for a national insurance company• Providing external and internal training on predictive modeling techniques, software and applications• Developing a predictive model for a corporate self-insurer to assist in setting more accurate early case reserves for their workers’ compensation claimsStochastic Reserving• Developing a bootstrapping model to quantify uncertainty around workers’ compensation liability reserve estimates for a large national corporationProject Management• Managing budget and day-to-day progress of cross-office reserving project with annual revenue over $500,000Past Positions: Actuarial Analyst - July 2012 - September 2014Actuarial Intern - May 2011 - May 2012