Economist

Key Skills

Python Programming, Bayesian networks, R Programming, Statistics, Econometrics, Stan

Job Description

Minimum qualifications:

• PhD in Economics or equivalent practical experience
• 2 years of experience in the tech sector or public policy
• Experience with causal inference
• Experience with econometrics, machine learning, and Bayesian statistics

Preferred qualifications:

• Experience in consulting
• Experience in programming (e.g., R, Stan, Python)
• Experience with models that use panel data
• Experience with Bayesian hierarchical models, Bayesian Additive Regression Trees (BART), Bayesian Causal Forest (BCF), Gaussian Processes, and Synthetic Control methods
• Experience designing and analyzing randomized controlled trials
• Experience designing and analyzing causal studies using observational data

About the job

We are a group of independent consultants under Google’s Chief Economist leveraging data through econometric techniques to inform decisions.

As an Economist on the Chief Economist Team, you will be an internal consultant tasked with helping Google make better decisions. You will be working with many other Google teams, such as Finance, Product and Engineering, Sales, Marketing, Legal, Policy, and Research. Specifically, you will speak on data's behalf to help decision-makers at Google make data informed choices. You will translate vague questions into research questions on which you can design a study and communicate findings to non-technical audiences, while being transparent about uncertainty and the assumptions used in your analysis.

Responsibilities

• Design and lead casual studies with the clear objective of answering business questions.
• Develop forecasts for key business and operational metrics.
• Effectively present and communicate actionable insights and recommendations to executives, business leaders, and cross-functional partners.
• Serve as a peer-reviewer and consultant for causal studies designed by other teams.
• Stay up to date with advances in the causal inference literature, and develop new methods when appropriate.