Meta is looking to hire a Data Scientist to shape the future of people-facing and business-facing products across its family of applications by leveraging its rich data sets to solve product development's biggest challenges and influence product strategy.
Requirements
- 2+ years of work experience in analytics and data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R)
- 2+ 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]
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
Other
- Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
- Bachelor's degree in Mathematics, Statistics, a relevant technical field, or equivalent
- Master's or Ph.D. Degree in a quantitative field
- Apply your technical skills, analytical mindset, and product intuition
- Collaborate on a wide array of product and business problems with a wide-range of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance and others.
