Experience with modern causal inference, statistical tools and concepts demonstrated in past projects, coursework, or publications.

Experience with synthetic controls and other panel data techniques, doubly robust estimators, and heterogeneous treatment effect estimators.

Experience designing and analyzing randomized controlled trials or observational studies, demonstrated in past projects, coursework, or publications.

Experience in causal inference, applied microeconomics, or econometrics demonstrated in projects, coursework, or publications.

About the job

In this role, you will be an internal consultant tasked with helping Google make better decisions through the use of causal inference. You will be working with many teams such as Finance, Product, Engineering, Business, Marketing, Legal, Policy, and Research. Your job will be to help decision-makers at Google make informed choices using econometric and statistical methods. You will have excellent communication skills in addition to technical expertise. You will translate vague questions into concrete problems on which you can design studies, and communicate findings to non-technical audiences while effectively conveying uncertainty and the assumptions used in your analysis.

You will have experience with modern methods in causal inference and Machine Learning (ML). Google uses a large set of tools from experimental to observational techniques, touching on a broad range of problems from different functional and product areas. When available methodologies are not well-suited for the problem you are trying to solve, you will need to develop new techniques and models.

The name Google came from "googol," a mathematical term for the number 1 followed by 100 zeros. And nobody at Google loves big numbers like the Finance team when providing in depth analysis on all manner of strategic decisions across Google products. From developing forward-thinking analysis to generating management reports to scaling our automated financial processes, the Finance organization is an important partner and advisor to the business.

The US base salary range for this full-time position is $127,000-$187,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Design and lead causal inference studies to answer critical business questions and evaluate effects of interventions.
• Present and communicate actionable insights and recommendations to executives, leaders, and cross-functional partners.
• Serve as a subject matter expert, reviewer and consultant for causal inference studies conducted by cross-functional teams.
• Develop and maintain internal tools and statistical software packages for investigative tasks and impact measurement.