The role
From Google's own posting.
Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.
As a part of the GCS Data Science team, you will be working on challenging yet interesting problems for Google's Global Business Organization (GBO). Your goal is to build efficient and scalable ML models that help small and midsize businesses around the world grow their business, leveraging the power of Google solutions.
In this role, you will be passionate about solving problems with the latest research in applied deep learning, causal inference and measurement theory. We work with product teams to understand their objectives, business requirements and constraints, and key metrics. We propose, build, evaluate and debug machine learning models and algorithms; we integrate our pipelines, models and predictions into production serving systems.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Design, develop, and validate robust causal inference models (e.g., Synthetic Control, Difference-in-Differences, Double Machine Learning) to isolate the incremental impact of GCS programs.
Partner with business teams to design and execute A/B tests, defining the sample sizes, power analyses, and success metrics required for valid results.
Track the latest academic research in Causal ML and Econometrics, proactively prototyping new methods to improve the precision of impact estimates.
Translate highly technical methodologies into clear, prescriptive business narratives for non-technical executive audiences.
Establish comprehensive monitoring systems to track model performance, detect data drift, and ensure the ongoing accuracy of deployed measurement frameworks.
Minimum qualifications:
Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
Preferred qualifications:
PhD in a quantitative discipline such as Computer Science, Engineering, Economics, Statistics, Mathematics, Physics, Neuroscience, or equivalent practical experience.
4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
Experience in driving a project from an experimental idea to a proof-of-concept to a launched product feature.
Experience in publications and working with technologies.
Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.