Data Scientist, Analytics-Growth Infrastructure
Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started.
Serving billions of people depends on advanced tools and systems to power products and enable analysis at scale. This role would help develop those central systems and services that power products, research and development across Facebook's largest products. These are data-intensive problems that deal with complex back-end systems, applied machine learning problems and analysis platforms used by data scientists, engineers and product teams across the company.
One important initiative in this area is driving quality and rigor across all of the company's external-facing metrics. This includes developing the central metrics infrastructure, and partnering with teams across the company to improve quality of metrics to the highest standard. This area is especially suited for somebody who enjoys diving deep into new areas and systems, driving best practices across product and analytics teams. This position is based in our Menlo Park offices.
- Provide technical and thought leadership on designing, prototyping, implementing and automating complex analyses
- Partner with cross-functional teams to identify new opportunities requiring the use of modern analytical and modeling techniques
- Effectively communicate insights and recommendations to upper management in support of strategic decision-making
- Develop and lead best practices for designing, tuning and evaluating complex measurement methodologies
- Plan, and be able to conduct as needed, end-to-end analyses, from data requirement gathering, to data processing and modeling
- Own ongoing deliverables and communications
- Work with engineers to architect, develop, and optimize data and modeling pipelines
- MS degree in a quantitative discipline (e.g., statistics, operations research, econometrics, computer science, applied mathematics, physics, electrical engineering, industrial engineering) or equivalent experience
- 10+ years experience doing quantitative analysis or statistical modeling
- Knowledge of at least one modeling framework (e.g., SciKit Learn, TensorFlow, SAS, R, MATLAB)
- Experience extracting and manipulating large datasets
- Development experience in any scripting language (PHP, Python, Perl, etc.)
- Proven experience to influence product strategy through data-centric presentations (to product, business, and other stakeholders)
- Knowledge in at least one of the following areas: predictive modeling, machine learning, experimentation methods
- 5+ years leading technical teams
- Experience with distributed computing (Hive/Hadoop)
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