Facebook’s mission is to give people the power to build community and bring the world closer together. Through their family of apps and services, they’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 they’re creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Their 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, they can help people build stronger communities – they’re just getting started.
Marketing Science R&D is an interdisciplinary team of data scientists focused on research that improves measurement and ads delivery on their platform. The team’s expertise spans domains, including causal inference, survey methodology, machine learning, cryptology, location and identity prediction algorithms.
They develop methodologies, design and prototype solutions, and partner with their engineering and product colleagues to scale these solutions such that millions of advertisers can benefit.
They’re looking for researchers and data scientists with expertise in causal inference to recognize new opportunities and help build highly-scalable, scientifically rigorous measurement and ads delivery systems. Challenges include using machine learning to scale variance reduction techniques to millions of advertisers’ experiments, to identify heterogeneous treatment effects with computational efficiency, to enable causal inference with diverse and incomplete observational data, and to ensure generalizability from partial or unrepresentative data. Team members will pursue deep technical knowledge of their ads and measurement systems at Facebook, and research new approaches and algorithms to advance the state of causal inference within advertising.
- Research opportunities to develop and implement new methods or algorithms for causal inference to improve ads delivery and measurement for advertisers
- Assess the validity and rigor of new data sources and approaches, establishing scalable validation frameworks for ongoing evaluation
- Work both independently and build cross-functional relationships with Engineering, Product and Analytics to shape long-term product roadmaps with a balance of technical rigor and strategic considerations
- Learn new tools, systems and languages quickly as required by the particular project you are working on
- Apply excellent communication skills to engage diverse audiences on technical topics and nuanced insights
- PhD in Economics, Political Science, Statistics, Psychology, or related field, or a Master’s within one of these fields combined with 3+ years of hands-on research experience in the social or biomedical sciences, internet or advertising industry architecting and implementing experimentation or causal inference-focused solutions
- Experience with at least two of the following: Experimental Design, Analysis of Experiments, Observational Causal Inference, and Quasi-Experimental Methods
- Experience addressing challenges that emerge from missing or unrepresentative data
- Knowledge with Python and/or R
- Causal inference-related patents, conference presentations, academic papers or other indicators of research proficiency in industry-grade causal inference
- Experience with online advertising, targeting, optimization and measurement systems
- Development experience specific to experimentation platforms
- Proficiency with machine learning, deep learning and advanced statistical modeling
- Experience with Stan or JAGS
- Experience with Presto, Hive or Spark SQL
- Salary Offer 0 ~ $3000
- Experience Level Junior
- Total Years Experience 0-5
- Dropdown field Option 1