Business Data Scientist, Applied Machine Learning, GCS

Google Inc.; New York (NY); On-site; USD 138,000 - 197,000; Full time

Benefits offered by this job

Equity

Bonus target

Job summary

Google's GCS Data Science team seeks a Business Data Scientist who builds scalable ML models to growth for small and midsize businesses worldwide. You will apply applied ML to measure impact and inform executive decisions within the Global Business Organization.

You will advance causal inference methods, translate insights into actionable business guidance, and collaborate with product teams to implement features and monitor outcomes at scale.

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.
• PhD in a quantitative discipline is preferred.

Responsibilities

• Design, develop, and validate robust causal inference models (e.g., Synthetic Control, Difference-in-Differences, Double Machine Learning) to isolate impact.
• Partner with business teams to design and execute A/B tests with appropriate sample sizes and power analyses.
• Track the latest academic research in Causal ML and Econometrics to improve impact estimates.
• Translate technical methodologies into clear business narratives for executives.
• Establish monitoring systems to track model performance and data drift.

Skills

Python

R

SQL

Education

Master's degree in a quantitative discipline

Tools

Causal ML techniques

Job description

Business Data Scientist, Applied Machine Learning, GCS

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Mid

Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

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info_outline X Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA.

• 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.

About the job

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.

About the job

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.

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy , Know your rights: workplace discrimination is illegal , Belonging at Google , and How we hire .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.

Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.