Data Scientist (Product)

Key Skills

3D data processing, Data science techniques, Database, ETL frameworks, Infrastructure as code, Leadership Skill, Machine learning techniques, Python Programming, SQL

Job Description

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.