Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. To apply, click “Apply to Job” online on this web page.
Responsibilities
Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches. Partner with Product and Engineering teams to solve complex problems and identify trends and opportunities. Inform, influence, support, and execute our product decisions and product launches. Work on Data Infrastructure; Working in Hadoop and Hive and MySQL. Authoring pipelines via SQL and python based ETL framework; Guide experimentation across the Core Ads Growth organization (~1,200 people for all functions). This entails reviewing complex experiments across the Core Ads Growth product suite, and supporting the development of robust measurement plans across organizational pillars Responsible for machine learning model development including guiding Machine Learning Engineers on the authoring of Machine Learning models such as guiding label design, and offline and online evaluation of results. Advancing analytics in Python and R leverage big data to make the right strategic business decisions, such as complex trade-offs between competing metrics, and steer product development for positive long-term outcomes for the business. The role, more specifically, involves the development and application of statistical methods to measure the long-term effects of changes and building key data sets to empower operational and exploratory analysis. Apply causal inference methodologies to analyze data and identify new opportunities for product changes. Designing and evaluating experiments, monitoring key product metrics, and understanding root causes of metrics changes. Building and analyzing dashboards and reports. Exploratory analysis is needed to understand users and what leads to positive long-term engagement. Working on roadmap building proposals and Understanding ecosystems, user behaviors, and long-term trends. Identifying levers to help move key metrics. Evaluating and defining metrics. Building models of user behaviors for analysis or to power production systems. Product Leadership and Influencing product teams through the presentation of work. Communicating the state of business, experiment results to product teams. Spreading best practices to analytics and product teams. Apply predictive modeling, machine learning, and experimentation / causal inference methods regularly to solve real life problems. 5% International Travel Required.
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Industrial engineering relevant technical field and five years of progressive, post-baccalaureate experience in the job offered or a computer-related occupation. Alternatively, the employer will accept 7 years of experience Experience must include 5 years of experience in the following: Leading analytics work in IC capacity, working collaboratively with Engineering and cross-functional partners, and guiding data-influenced product planning, prioritization and strategy development Applying predictive modeling, machine learning, and experimentation / causal inference methods on a regular basis to solve real life problems Working effectively with a very large number of stakeholders, cross functionally, including Engineering, PM/TPM, Analytics & Finance as well as cross-org Framing and communicating with senior leadership