Data Scientist, Infrastrategies Responsibilities
- Collect, organize, interpret, and summarize statistical data in order to contribute to the continued growth of Meta's infrastructure.
- Apply experience in quantitative analysis and data mining to improve, optimize, and expand Meta’s infrastructure across a variety of domains with an emphasis on long-term and strategic initiatives.
- Work cross-functionally as a strategic partner to define priorities and develop project roadmaps in synergy with partner teams.
- Build consensus and earn commitment from partners.
- Drive execution through fast iteration.
- Ensure coordination of projects across related workflows to maximize impact and avoid duplication and overlaps.
- Drive efficient data exploration and modeling.
- Build pragmatic, scalable, and statistically rigorous solutions to large-scale web, mobile and data infrastructure problems by leveraging or developing statistical and machine learning methodologies.
- Generalize methodologies for broader application within and outside domain.
- Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors.
- Demonstrate good judgment in selecting methods and techniques for obtaining solutions.
Minimum Qualifications
Requires a Master's degree in Computer Science, Economics, Engineering, Information Systems, Analytics, Mathematics, Physics, Applied Sciences, or a related field and one year of work experience in the job offered or in a computer-related occupation.
Requires one year of experience in the following:
- Performing quantitative analysis including data mining on highly complex data sets
- Data querying language: SQL
- Scripting language: Python
- Applied statistics or experimentation, such as A/B testing, in an industry setting
- Machine learning techniques
- ETL (Extract, Transform, Load) processes
- Relational databases
- Large-scale data processing infrastructures using distributed systems, AND
- Quantitative analysis techniques, including one of the following: clustering, regression, pattern recognition, or descriptive and inferential statistics.