Business Data Scientist, Google Customer Solutions Ads Finance
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Minimum qualifications:
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, 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.
Preferred qualifications:
- 6 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
- Experience with modeling and analysis, and understanding of application of statistics and data science.
- Experience in SQL, and familiarity with Python or R.
- Ability to translate complexity and explain analysis and methodologies to both engineering and business stakeholders.
- Ability to grasp business concepts, apply business insights to decision frameworks and model development.
- Excellent partnering skills, and ability to navigate ambiguity.
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 Business Data Scientist, you will have an opportunity to directly shape the incentives and futures of our business. You'll contribute to programs and continuously develop your data science skills. You'll explore time series analysis, examine business drivers, and help build the foundational infrastructure for business analytics and research.
Responsibilities
- Incorporate business insights into incentives models, develop frameworks, driver models and conduct analysis.
- Develop statistical hypotheses and build predictive models.
- Build and work with complex datasets effectively. Create and maintain solutions using SQL, Python or R.
- Work with business teams to influence program design and explain goal setting and investigative methodologies.
- Work with technical teams to integrate goals and other data sets into critical systems like Connect Sales.
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