We explore the art of identifying and verifying assumptions as we build and deploy data science algorithms into production systems. These assumptions can take many forms, from the typical “have we properly specified the objective function?” to the much thornier “does my partner in engineering understand what data I need audited?”. Attendees from outside industry will get a glimpse of the complications that arise when we fail to tend to assumptions in deploying data science in production systems; those on the inside will walk away with some practical tools to increase the chances of successful deployment from day one.