Meta is seeking a Data Scientist to join the data science 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. This position will use data and analysis to identify and solve product development's biggest challenges and will require an understanding 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 high-performing analytics community dedicated to skill development and career growth in analytics and beyond.
Responsibilities
Work with large and complex data sets to solve a wide array of problems using different analytical and statistical approaches Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to build and maintain end-to-end models for long range planning and strategic decisions Build models to compute and explain Infrastructure OPEX and CAPEX costs at the company, product and resource levels Leverage understanding of AI and Infrastructure to develop point-of-view on ROI of investments in Infrastructure and allocation of Infrastructure resources to various products and software platforms Identify and measure success infrastructure investments through goal setting, forecasting, and monitoring of key metrics to understand trends Help define resource allocation policies that are reasonable and actionable from a technical, operational and financial perspective Work with product, engineering and data science teams to do technical, operational and business impact assessments of re-allocation of resources based on changing business needs, competitive landscape and product roadmaps Maintain lineage of decisions around Infrastructure investments and assumptions under which those decisions were made to drive accountability for outcomes across the company Define, understand, and test opportunities and levers to improve the our models, and drive roadmaps through your insights and recommendations Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions
Qualifications
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.) Experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), and/or statistical/mathematical software (e.g., R) Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies 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 large customer of a public cloud company Experience partnering cross-functionally with a wide range of teams, dealing with ambiguous and presenting technical content in an easy to understand manner to technical and non-technical teams Knowledge of business outcomes and technology investments and experience connecting them to practical models for decision making