Job description

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. Global Business Strategy and Operations (GBS&O) is part of the Go-to-Market organization making sure Google's business pursues the best strategy and executes flawlessly. GBS&O architects the future of Google’s global ads business, consisting of highly pragmatic and results-oriented investigative and experts in business operations. The Data, Insights, and Analytics (DIA) team partners with GBS&O team members to drive the best decisions possible with data. We build creative ML and Data Science solutions to drive strategy and business impact. We help our organization make decisions to ensure the Ads Business continues to thrive. In this role, you will help GBS&O partners make decisions using data owning the full data science life-cycle for high-impact, ambiguous business problems, from initial analysis to strategic recommendation. You will drive alignment and simplicity to manage a range of high-impact projects (e.g., causal inference, predictive modeling) initially focusing on projects, including seller incentives design and impact analysis, working closely with Strategy and Finance partners. You will own the technical infrastructure and investigative roadmap for systems and methodologies that measure effectiveness of our incentives programs to drive the executive-level roadmap requiring flexibility to pivot based on evolving business needs. 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 . • Lead the analytics, data infrastructure, and measurement methodology of seller incentives, designing data pipelines and tooling to executing advanced statistical analysis and generating insights. • Partner with Strategy and Operations and Finance stakeholders, who own the end-to-end processes for incentives design or Ads policy, to translate high-priority business problems into clear, data-driven strategies and decisions. • Own the full data science life-cycle, using your causal inference, predictive modeling, and descriptive analytics expertise to build frameworks that solve business problems. • Manage ambiguous problems working with datasets, using SQL, R, and Python to find creative, scalable solutions and build automated data pipelines, BI infrastructure, and dashboards from the ground up. • Act as a data storyteller and partner, translating findings into compelling narratives and recommendations that influence and align executive leaders and teams (Strategy, Product, Engineering). • 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. 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: • 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 designing and implementing causal inference methods. • Experience building and maintaining BI infrastructure, dashboards, and data pipelines. • Proficiency in database querying (SQL). • Excellent communication, presentation, and stakeholder management skills, with the ability to explain concepts to audiences.

Requirements

Must-haves

In this role, you will help GBS&O partners make decisions using data owning the full data science life-cycle for high-impact, ambiguous business problems, from initial analysis to strategic recommendation.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Lead the analytics, data infrastructure, and measurement methodology of seller incentives, designing data pipelines and tooling to executing advanced statistical analysis and generating insights.
Own the full data science life-cycle, using your causal inference, predictive modeling, and descriptive analytics expertise to build frameworks that solve business problems.
Manage ambiguous problems working with datasets, using SQL, R, and Python to find creative, scalable solutions and build automated data pipelines, BI infrastructure, and dashboards from the ground up.
Act as a data storyteller and partner, translating findings into compelling narratives and recommendations that influence and align executive leaders and teams (Strategy, Product, Engineering).
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.
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.
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.

Nice-to-haves

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits
Experience in designing and implementing causal inference methods.
Experience building and maintaining BI infrastructure, dashboards, and data pipelines.
Proficiency in database querying (SQL).
Excellent communication, presentation, and stakeholder management skills, with the ability to explain concepts to audiences.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

gomlsqlpython

Platform: google
Effort: medium