Data Scientist, Infrastructure Finance (Technical Leadership)

Meta is seeking a Data Scientist to join a newly formed team in the Finance organization that partners very closely with Product, AI, Infrastructure, Finance and other Data Science teams across the company. These teams are building some of the most cutting edge and transformative AI products in the world that are being rolled out to Meta's 3 Billion+ users. Building these products and features requires tens of billions of dollars of capital each year over a sustained period of time. Managing and optimizing the deployment of this vast capital and the allocation of these resources requires a team that has technical expertise in AI and Infrastructure along with a solid understanding of data science, finance and operations. We are building this team in recognition of the importance of AI and Infrastructure from a product perspective as well as the need to be efficient in our approach to deploying capital and generating returns for our shareholders.

This position will use data and analysis to identify and solve product development's biggest challenges and will require one to understand the technical aspects of how AI and Infrastructure are built, operated and used to serve users. This role will help establish the ROI and company-wide prioritization of such investments and work on solving some of the most important technological problems of our times and also ensure that the company makes efficient investments.

As an individual contributor, you will influence product strategy and investment decisions with data, be focused on impact, and collaborate with other teams. By joining Meta, you will become part of a world-class analytics community dedicated to skill development and career growth in analytics and beyond.Bachelor's degree in a directly related field, or equivalent practical experience A minimum of 12 years of work experience in analytics (minimum of 8 years with a Ph.D.) Bachelor's degree in Mathematics, Statistics, a relevant technical field, or equivalent practical experience Experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical/mathematical software (e.g., R) Master's or Ph.D. degree in a quantitative field Experience working in a data science role at a hyperscaler, public cloud, and/or a customer of a public cloud company Experience partnering cross-functionally with a wide range of teams, deal with ambiguity and present technical content in an easy to understand manner to technical and non-technical teams Curiosity about the inter-relationship between business outcomes and technology investments and experience translating this to practical models for decision making