Data Scientist - Inference
At TripAdvisor, we never stop learning. This is true both individually and as a company, where constant experimentation helps us to understand how we can continue to grow and improve the user experience for our 500 million unique monthly visitors worldwide. Data-driven decision making is at the core of what we do.
This position is based in either Needham, MA or Lisbon, Portugal
We are looking for an experienced data scientist/statistician to join our B2C Data Science team. As a Data Scientist focusing on Inference, you will be responsible for developing and implementing best practices in the design of experiments, A/B testing, the analysis of observational data, panel data analysis, time-series modeling, and more. You will collaborate with data scientists, analysts, product managers, and engineers to determine the best course of action for testing and experimentation across a multitude of initiatives and products. Your work will help determine which features and enhancements we deploy online, allowing you to directly observe the impact of your work in real time as you help the company grow.
We are looking for someone who is a fast learner, independent, and curious, who is passionate about data science and statistics. You will be working in a diverse environment where you’ll operate at the intersection of Data Science, Analytics, and Revenue Management. You will be encouraged to take ownership of your projects and to find new opportunities and problems where experimentation could be applied to drive the business forward. Additionally, you will be responsible for staying up to date on the literature, keeping up with state-of-the-art statistical research and methodology.
PhD in Statistics/Biostatistics, Economics, Mathematics, or related field.
Excellent communication skills for both technical and non-technical audiences.
Expertise in causal inference (propensity score methods, double ML, matching methods, etc.) and/or experimental/quasi-experimental design.
Experience with time-series modeling and analysis of repeated-measures data.
Proficiency in Python and/or R.
Experience with advanced machine learning methods a plus.
Experience working with real-world data strongly preferred.
Previous industry experience strongly preferred.