- Collect, analyze, and interpret statistical data in order to contribute to the design and development of Meta products.
- Partner with Product and Engineering teams to handle dynamic business and regulatory needs.
- Inform, influence, support, and execute our product decisions and launches.
- Work on problems of diverse scope including, but not limited to, product influence, developer experience, and data privacy.
- Ensure coordination of projects across related workflows to maximize impact and avoid overlaps.
- Build pragmatic, scalable, and statistically rigorous solutions to large-scale web, mobile and data infrastructure problems by leveraging statistical and machine learning techniques.
- Synthesize output of models to create business-relevant conclusions and identify the critical implications.
- Design studies choosing appropriate methods to test hypothesis for product launch.
- Eliminate data bias to make sure data accurately represents the population of interest.
- Design success metrics that measure short and long-term progress for both goal and project performance.
- Set up metric goal to inform team roadmaps and priorities.
- Identify gaps between system components and designs and deliver solutions.
- Appropriately apply advanced procedures to solving complex data visualization problems.
- Requires a Master's degree in Computer Science, Engineering, Industrial Engineering, Operations Research, Information Systems, Analytics, Mathematics, Physics, Applied Sciences, or a related field and 2 years of experience in the job offered or a related occupation.Experience must include 2 years of experience in the following:
- 1. Performing quantitative analysis including data mining on highly complex data sets
- 2. Data querying language: SQL
- 3. Scripting language: Python
- 4. Statistical or mathematical software including one of the following: R, SAS, or Matlab
- 5. Applied statistics or experimentation, such as A/B testing, in an industry setting
- 6. Machine learning techniques
- 7. ETL (Extract, Transform, Load) processes
- 8. Relational databases
- 9. Large-scale data processing infrastructures using distributed systems
- 10. Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics.
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