Staff Business Data Scientist, Forecasting

GoogleMountain View, CA
8d

About The Position

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. In this role, you will own and execute critical global finance processes—including business forecasting, planning, sales incentives, capital allocation, and seller incrementality—delivering scalable solutions that ensure the broader team works efficiently. You will also shape the narrative for the Global Business Organization in key leadership moments. Furthermore, you will leverage Artificial Intelligence-powered solutions to advocate standardization and automation, creating capacity for analysts to focus on high-impact partnering and proactively defining the future of the Finance organization for the Ads business.

Requirements

  • Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  • 7 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.

Nice To Haves

  • 9 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.

Responsibilities

  • Develop metrics to track and evaluate solution deployment across teams.
  • Create dashboards and tools to automate processes, generate reports, and guide product decisions.
  • Curate and validate data to ensure quality standards are met.
  • Collaborate with stakeholders to understand the domain, business goals, and data infrastructure context.
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