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Drive the research direction in developing methods and tools that increase the rigor and efficiency of our experimentation platform and analyses that are constructed using causal inference techniques.
Lead education initiatives by conducting literature reviews and by promoting the adoption of good statistical practices through efforts such as instructing at Twitter University and building new processes.
Write and collaborate on production code in Scala to implement new methods.
Consult with other teams across Twitter on complicated experimental designs and on analyses estimating causal effects.
Significant experience and excitement with one or more of the following: advanced statistical techniques for A/B testing, methods for experimental design, observational causal inference, or quasi-experimental analysis. Examples include: quantile testing, sequential testing, variance reduction techniques, variance estimation for ratio metrics, multi-level / hierarchical modeling, statistical surrogate modeling, matching methods, regression adjustment, structural equation models, instrumental variables, regression discontinuity design, and graphical approaches to causal inference.
Track record for executing independent projects and leading complex, multi-functional projects with several dependencies.
Strong proficiency with Python / R and SQL.
Excitement to work with production engineering systems that are written in Scala and Scalding.
Experience with scaling experimentation systems and Spark is a plus.
Job opportunities should be equal. We don't discriminate. Period. In legal terms, that means: Twitter is an equal opportunity employer and doesn’t discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any other legally protected status.
San Francisco applicants: In response to the San Francisco Fair Chance Ordinance, we’d like to mention that we consider qualified applicants with arrest and conviction records.
