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Meta

Finance Manager, MSL Cross-Finance

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What they do

A Financial Manager directs and plans the financial activities of a company or organization. Prepares financial statements and business activity and forecasting reports; supervises accounting and preparation of financial reports; advises management with regard to finance decisions.

$168,065 / year median in California

-4% projected decline

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Job Description

We are looking for a Finance Manager to join the Meta Superintelligence Labs (MSL) Finance team. This role will drive the end-to-end consolidation and FP&A operational processes for Meta's MSL spend, working with finance partners, accounting, and corporate finance to deliver accurate, timely financial reporting and forecasting to senior leadership. This is a high-visibility role working on one of the top priority areas for Meta.
Qualifications:
9+ years of experience in FP&A Experience leading close and/or forecast processes in a large, matrixed organization Strong analytical skills with the ability to synthesize large volumes of financial data into clear narratives for senior leadership Ability to prioritize competing workstreams based on close cycle deadlines, impact, and stakeholder needs Cross-functional communication skills and ability to drive alignment across teams with competing priorities Advanced proficiency in financial modeling and data analysis tools Ability to work independently with limited guidance, across multiple time zones with both Finance and key cross functional business partners Experience preparing materials for VP-level or C-suite reviews Master's degree in business, finance, accounting, management or related field Experience with Financial systems (e.g. Essbase, Oracle Financials), dashboard reporting tools (i.e. Tableau) and SQL Experience in supporting an AI lab or technology company with a focus on AI 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