Max Schmeiser

I lead Data Science at Twitter. Our team's mission is to ensure that every decision at Twitter is informed by accurate data and rigorous empirical analysis. We work backwards from a deep understanding of business problems and objectives to drive business impact through the application of the appropriate technique from the data science toolkit (experimentation, observational causal inference, ML, forecasting, analytics, optimization, etc.).

I built and led the 80+ person Data Science organization at Convoy, which is working on transforming the $800 billion trucking industry. The team of research scientists, economists, data engineers, operations research scientists, and data scientists builds the economic, machine learning, and optimization models that power freight pricing, shipment offer relevance, auction bidding strategy, operational automation, and other core services of the platform. They also forecast business metrics, evaluate the causal impact of business decisions and processes, design experiments to optimize the product, and leverage Convoy's data to generate insights for our customers.

Led a 45-person+ team of data scientists, machine learning scientists, economists, data engineers, survey methodologists, and UX researchers who were reinventing how employee pulse surveys can inform People Analytics and drive business impact. We leveraged our ownership of Amazon's daily employee pulse survey (Connections), Amazon-wide data, machine learning, and econometric analysis to help businesses throughout Amazon identify opportunities and efficiencies at scale. We further used these data to generate innovative new People insights and improve the accuracy of predictive models for Human Resources outcomes.

I managed all data science and economics teams for Amazon Lending. These included the Risk Management, Machine Learning, Causal Inference, Credit, and Business Insights teams for our 3P Seller Lending, Invoicing, and other Lending businesses. I also managed a team of economists and data scientists who used machine learning and econometric analysis to enhance the businesses of Amazon Marketplace's third-party merchants.

I headed the Risk & Analytics unit at Amazon Lending and managed analytics teams for other Marketplace businesses. At Lending, my team of economists, data scientists, and business intelligence engineers was responsible for measuring, monitoring, and mitigating credit and operating risks across all Lending products globally. My team was also responsible for econometric analysis and predictive modeling (machine learning) to inform and evaluate credit models, credit policy, loan sizing, pricing, and other decisions. I further managed a team of economists who did predictive modeling, pricing, and causal inference for Amazon Home Services, and a team of research scientists who used machine learning to enhance the performance of Marketplace Sellers.

• Supported production of the Board’s triennial Survey of Consumer Finances (SCF) and provided technical assistance on other Board surveys. • Conceived, designed, deployed, and produced the Board's first Survey of Household Economics and Decisionmaking (SHED) and corresponding Report on the Economic Well-Being of U.S. Households. • Conducted economic research on consumer finances, financial literacy, and financial behavior. • Presented research and analysis to Federal Reserve Board governors, senior management, financial industry personnel, consumer groups, and academics. • Published research in academic journals and Board publications. • Produced reports and presentations based on survey data for public dissemination. • Wrote speeches and prepared Congressional testimony for Federal Reserve Board governors and senior management.

• Conducted economic research in the areas of household finance and consumer behavior, with an emphasis on mortgage finance and financial literacy. • Conducted research and analysis to support the Federal Reserve Board’s work on financial regulations, bank supervision, and consumer protection. • Presented research and analysis to Federal Reserve Board governors, senior management, financial industry personnel, consumer groups, and academics. • Published research in academic journals and Board publications. • Designed, deployed and analyzed surveys of consumers’ financial behavior and use of financial products and services, including the Board's annual Survey of Consumers' Use of Mobile Financial Services. • Produced reports and presentations based on survey data for public dissemination. • Wrote speeches and prepared Congressional testimony for Federal Reserve Board governors and senior management. • Supervised a team of research assistants.

• Taught core courses in Department’s Personal Finance major, including “Household Risk Management” (insurance) and “The Consumer and the Market” (applied microeconomics). • Taught PhD course in Family Economics. • Conducted research broadly focused on how public policies affect the health status and economic wellbeing of vulnerable populations. • Wrote grants to fund my research, and obtained over $500,000 from various granting agencies and foundations in less than three years. • Managed projects and budgets to meet grant objectives. • Published research in academic journals, books, and other forums. • Managed a team of four to six research assistants working on various funded research projects. • Presented research findings at academic conferences and to various government and non-profit agencies. • Served as a media source on various topics. • Served as a University of Wisconsin Faculty Senator and on the University of Wisconsin System Insurance Board. • Mentored undergraduate and graduate students, and supervised graduate students’ theses.