Business Data Scientist II, Google Data
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Minimum qualifications:
- Bachelor's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.
- Experience solving product or business problems using investigative, machine learning, artificial intelligence, or statistical methods, including coding (e.g., Python, R, or SQL).
Preferred qualifications:
- 2 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.
About the job
Our mission is to solve the complex needs of gTech Strategy and Operations by using data science to make Google Ads products more usable for customers. By pioneering advanced solutions in machine learning, forecasting, and generative AI, the team empowers customers, agents, and internal partners. Our approach involves identifying key business challenges, developing innovative models, and collaborating with partners to translate complex findings into actionable insights.
The US base salary range for this full-time position is $118,000-$170,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.Responsibilities
- Support the development and implementation of data-driven solutions, with a focus on large language models (LLMs) and generative AI, to solve key business problems. Help optimize support operations, automate case routing, and make Google Ads products more usable for our customers.
- Collaborate with cross-functional partners to identify challenges and opportunities, translating business needs into actionable data science solutions.
- Contribute to problem solving and solution translating, leading projects from ideation to implementation and ensuring complex findings are communicated to both technical and non-technical stakeholders.
- Apply emerging trends in data science to take on the problems.
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