Senior Research Data Scientist, Ads Insight and Measurement

About the role

MINIMUM QUALIFICATIONS:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics,

Operations Research, Engineering, or a related quantitative field.

• 5 years of work experience using analytics to solve product or business

problems, coding (e.g., Python, R, SQL), querying databases or statistical

analysis, or 3 years of work experience with a PhD degree.

PREFERRED QUALIFICATIONS:

• 8 years of work experience using analytics to solve product or business

problems, coding (e.g., Python, R, SQL), querying databases or statistical

analysis, or 6 years of work experience with a PhD degree.

• Experience articulating and translating business questions and using

statistical techniques to arrive at an answer using available data.

• Experience with causal inference methods such as split-testing, instrumental

variables, difference-in-difference methods, fixed effects regression, panel

data models, regression discontinuity, matching estimators.

• Experience with statistical data analysis such as linear models, multivariate

analysis, stochastic models, sampling methods.

• Applied experience with machine learning on datasets.

ABOUT THE JOB:

Google’s advertising measurement team focuses on combining data at scale with

formal science to make this possible. Our science helps make advertising useful

and delightful to our users, and valuable and results-driven for our advertisers

and publishers.

As a part of this team, data scientists bring scientific statistical methods to

bear on the challenges of advertising product creation, development and

improvement with a deep, data-driven appreciation for the behaviors of the end

user and the ecosystem. As a Data Scientist working on Ads Insights and

Measurement, you will develop, evaluate and improve the entire range of Google's

advertising products including Search, Display, Apps, TV and Video (YouTube).

You will collaborate closely with a multi-disciplinary team of engineers,

analysts and product managers to develop new science and to translate it into

deployed products at scale. You will also play a key role in developing new

ideas and methods that drive ad measurement and monetization, including

paradigm-shifting ad-measurement science and products for the privacy-preserving

future of digital advertising. You will be a key part of building and driving

impact on large-scale ad-systems both at Google and in the ad-tech and mar-tech

industry as a whole, globally.

In this role, individuals with an interest in understanding consumer behavior,

advertising and privacy, a passion for business problems, and an interest in

combining data and strategy will grow in the environment. You will be able to

leverage their training and skills to leverage data and technology to make

business decisions.

Google Ads is helping power the open internet with the best technology that

connects and creates value for people, publishers, advertisers, and Google.

We’re made up of multiple teams, building Google’s Advertising products

including search, display, shopping, travel and video advertising, as well as

analytics. Our teams create trusted experiences between people and businesses

with useful ads. We help grow businesses of all sizes from small businesses, to

large brands, to YouTube creators, with effective advertiser tools that deliver

measurable results. We also enable Google to engage with customers at scale.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• Direct the design and development of innovative measurement methodologies and

products, setting the strategic technical direction for Conversion Lift and

incrementality measurement.

• Architect and oversee the execution of complex experimental frameworks,

including both user-level and geo-based randomized controlled trials, to

rigorously establish causal impact at scale.

• Advance quantitative methods by integrating causal inference, statistical

modeling, and machine learning techniques to solve highly ambiguous and

complex measurement challenges.

• Drive the creation of scalable analysis pipelines, setting technical

standards for the team and mentoring other data scientists on end-to-end

analysis best practices.

• Serve a key technical lead and trusted cross-functional partner, and a mentor

to junior data scientists.