Data Science at Instacart: Making On-Demand Profitable
VP of Data Science at Instacart
- How Instacart has used data science to optimize last-mile delivery and balance supply and demand to drive efficiency gains that are transforming unit economics
- How improvements in predicting outcomes, batching algorithms, and forecasting have contributed to delivery efficiency
- How data science is organized at Instacart and how it collaborates with product, engineering, and field operators to make rapid innovation possible
Jeremy is currently the VP of data science at Instacart, where he works closely with data scientists who are integrated into product teams to drive growth and profitability through logistics, catalog, search, consumer, shopper, and partner applications. Previously, Jeremy was chief data scientist and EVP of engineering at Sailthru, which builds data-driven solutions for marketers to drive long-term customer engagement and optimize revenue opportunities. As chief data scientist, he was responsible for the intelligence in the marketing personalization platform, which included prediction, recommendation, and optimization algorithms. As EVP of engineering, Jeremy led development, operations, database, and engineering support teams and partnered with the CTO to drive innovation and stability while scaling.
Earlier in his career, Jeremy was the CTO of Collective, where he led a team of product managers, engineers, and data scientists in creating technology platforms that used machine learning and big data to address challenging multiscreen advertising problems, and he founded and led the Global Markets Analytics Group at Ernst & Young (EY), which analyzed the firm’s markets, financial and personnel data to inform executive decision making. His background in data-driven technology products spans a decade consulting with numerous global financial services firms on predictive modeling applications as a leader in the customer analytics advisory practice at EY.