This dissertation studies operations strategy on online platforms. Platform operations comprise three defining features: the growing use of algorithms and artificial intelligence; large numbers of users with competing interests and incentives for strategic behavior; and large amounts of data. The following three chapters study the design of data-driven policies to improve the operations of online platforms in competitive settings. Chapter 1 studies how gig economy workers make strategic decisions about where and when to work. We empirically measure two types of strategic behavior: multihoming, the choice of an online platform, and repositioning, the choice of a physical location. Using a comprehensive dataset that tracks worker activity across platforms, we develop and estimate a structural model to analyze how workers optimize their earnings by switching between platforms and locations in search of the best trips. We show that workers are highly heterogeneous in their preferences and find multihoming especially costly, both in absolute terms and relative to the cost of repositioning. Through counterfactual simulations, we show that firms and regulators can substantially improve system efficiency by enabling workers to freely multihome, as both workers' hourly earnings and service levels increase in equilibrium. We show that strategic behavior in the gig economy imposes large costs on both individuals and the overall market and that policies to ease the burden of strategic decision-making can significantly improve efficiency. Chapter 2 studies whether operational transparency can improve the impact of algorithmic pricing on customers and workers in the gig economy. We empirically measure the extent of algorithmic bias in pricing on ridehail platforms and model how workers strategically respond to algorithm-generated prices. We show that algorithmic prices vary significantly from any definition of a transparent price that can be explained by observable features of a trip. Despite this variation, we find that algorithmic pricing does not result in bias against low-income locations, as ridehail platforms offer higher service levels and lower prices relative to taxis. However, variation in algorithmic pricing incentivizes workers to decline a large portion of trips in practice, significantly decreasing system efficiency. We estimate policy counterfactuals and show that minimum wage and transparent pricing policies can improve efficiency without harming fairness constraints. Chapter 3 studies methods for using machine learning to generate interpretable causal inference estimates from online datasets. In machine learning settings, prediction errors are a commonly overlooked problem that can bias results and lead to arbitrarily incorrect parameter estimates. We consider a two-stage model where (1) machine learning is used to predict variables of interest, and (2) these predictions are used in a regression model for causal inference. Even when the model specification is otherwise correct, traditional metrics such as p-values and first-stage model accuracy are not good signals of correct second-stage estimates when prediction error exists. We show that these problems are substantial and persist across simulations and an empirical dataset. We propose general sample-based methods to identify when prediction errors are significantly biasing estimates and provide consistent corrections for the case where unbiased training data is available for the machine learning dataset.