Note: The job is a remote job and is open to candidates in USA. Meta builds technologies that help people connect, find communities, and grow businesses. As a Data Scientist at Meta, you will shape the future of products across their applications by applying your analytical skills to influence product strategy and drive insights for billions of users.
Responsibilities
- Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches
- Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses
- Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends
- Define, understand, and test opportunities and levers to improve the product, 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
Skills
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Bachelor's degree in Mathematics, Statistics, a relevant technical field, or equivalent
- 4+ years of work experience in analytics, data querying languages such as SQL, scripting languages such as Python, and/or statistical mathematical software such as R (minimum of 2 years with a Ph.D.)
- 4+ years of experience solving analytical problems using quantitative approaches, understanding ecosystems, user behaviors & long-term product trends, and leading data-driven projects from definition to execution [including defining metrics, experiment, design, communicating actionable insights]
- Master's or Ph.D. Degree in a quantitative field
- 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
Benefits
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